Building extraction from airborne LiDAR data is significant for the accurate modeling of urban structures and constitutes a pivotal step in urban 3D reconstruction. However, in complex scenarios with noise, uneven point density, and the coexistence of buildings with sparse vegetation or shrubs, existing geometric features (e.g., curvature) struggle to distinguish planar from non-planar regions, leading to extraction errors. To overcome this limitation, we propose the Adaptive Tensor-Weighted Planar Descriptor (ATPD), a novel planarity feature that employs second-order tensors and reconstructs the covariance matrix through a data-driven adaptive weighting scheme. This design significantly improves the separability of planar and non-planar regions under challenging conditions. Comparative experiments further confirm that ATPD consistently outperforms traditional geometric features such as roughness, omnivariance, planarity, and curvature. In addition, planar features alone are insufficient to capture regions with local variations (e.g., roof ridges). To address this, we integrate planar and mesh-based features and develop a multi-feature graph-cut algorithm for robust building extraction from large-scale point clouds. The proposed method is validated on three benchmark LiDAR datasets of Vaihingen provided by ISPRS, where it achieves the best performance with a quality of 90.3% and an F1-score of 94.9%, outperforming traditional methods. Compared with deep learning approaches, our method also yields higher correctness. Further experiments on large-scale datasets from Vaihingen, Germany, and Palmerston North, New Zealand confirm its robustness, successfully extracting all buildings larger than 50 m².

Given water-surface uncertainty problem, it is difficult for green lasers to detect the exact water surface. The near water surface penetration (NWSP) of green laser in water is not beneficial for high-accuracy water depth measurements but provides a possible mean for water–land discrimination. A novel water–land discriminator based on the NWSP of green laser is proposed in this study. The performance of water–land discriminator based on NWSP is evaluated using water–land interface derived by the traditional waveform clustering. Water–land discriminator based on NWSP can reach an overall accuracy of 99.45%. The proposed method which needs IR and green laser point clouds is recommended for water–land discrimination with high-accuracy in coastal and inland waters.

Ocean-land waveform classification is a crucial step in processing of airborne LiDAR bathymetry (ALB) data and can be used for waterline extraction. Special laser waveforms induced by complex environments cause errors in labeling oceans and land based on differences in waveform features of infrared (IR) lasers. These special laser waveforms cannot be identified based on differences in waveform features or elevation and can only be corrected using spatial information. The traditional density clustering algorithm is too time-consuming and not easy to implement for the large amount of ALB data. In this paper, a correction method is proposed based on the continuity of laser pulses emitted by ALB. Experiments demonstrate that the proposed method corrects mislabeled waveforms with 57% reduction in the number of mislabeled waveforms compared to the K- means method and 99.3% reduction in time compared to the dual-clustering method.

This paper tackles the problem of requiring a large amount of data annotation in LiDAR point cloud semantic segmentation (PCSS) task by proposing OPOCA, a weakly supervised network that only annotates one point per class in a single LiDAR scan. To compensate for the supervisory losses due to extremely few annotated labels, a large number of pseudo labels is first generated using a Pseudo Label Spreading Module (PLSM), whereas the potential ambiguity and inaccuracy is further addressed by a carefully-designed Spread Distance Loss (SDL) and a Range Image Auxiliary Module (RIAM). Moreover, we propose an iterative Self Training Module (STM) to increase the high-quality pseudo labels for the next round of training. Extensive experiments on various benchmark datasets (SemanticKITTI, Waymo Open dataset, and SemanticPOSS) demonstrate the rationality of each module and the superior performance of the proposed network over the current baseline with below 0.1 ‰ labels.

The real-time data acquisition system (RTDAQS) in the bathymetric light detection and ranging (LiDAR) instrument, named “GQ-Cormorant 19”, previously developed by our group cannot completely acquire the echo data from the water surface and water bottom owing to the inaccurate determination of the instant of echo signal acquisition, resulting in echo data loss. Therefore, this study developed an adaptive echo signal acquisition method based on a field-programmable gate array (FPGA) chip. The proposed method utilizes the flying height for roughness adjustment, and the peak of echo signal detection and the fine adjustment are combined to accurately determine the instant of echo signal acquisition. The improved RTDAQS onboard the GQ-Cormorant 19 was mounted on unmanned aerial vehicle (UAV) and unmanned surface vessel (USV) platforms and the performance was verified through an indoor corridor and several outdoor water fields. The experimental results demonstrated that the improved RTDAQS can improve the effective echo data rate by 11.5% when compared with the previously developed RTDAQS. Therefore, it can be concluded that the improved RTDAQS can implement the adaptive high-speed acquisition of echo data and obtain high-quality echo data acquisition on various platforms, such as UAV and USV.

The large field of view (FOV) of fisheye cameras makes them widely applied in many fields. However, a fisheye image contains bigger distortions than a traditional image does, resulting in traditional algorithms of the feature point-based image matching cannot simply be transplanted to a fisheye image. For this reason, an algorithm for feature point pseudo-neighborhood extraction is proposed. This idea is based on many ray beams around a feature point having the same incident FOVs when imaging with a fisheye camera. Thus, the model starts from incident ray beams of FOV around a given feature point. The algorithm first establishes the initial FOV-curves on the projection surface of the fisheye hemisphere, and then constructs an extended 2-D curve, called “FOV-curve network.” Finally, the feature point pseudo-neighborhood is constructed using the FOV-curve network. The four datasets are used to validate the proposed algorithm. The experimental results demonstrate that the average improvement rate of the matching point pairs for three datasets in outdoor scenes reached 23.5%, 319.6%, and 86.6% in the center zone, the left margin, and the right margin zone, respectively. The biggest one is for Dataset-2, which reaches 27.8% and 714% in the center and left margin zone when compared with three types of traditional speeded-up robust feature (SURF), FAST, and ORB algorithms. An average improvement rate relative to the deep learning, SuperGlue model, for three datasets in outdoor scenes are 2.8%. These experimental results demonstrate that the proposed algorithm effectively eliminates the impact of fisheye image distortion on feature point-based matching.

The previously presented self-organizing pixel entanglement neural network (SOPENN) model only establishes 2-D basis vectors that are orthogonal to each other in the Hilbert space, which cannot sufficiently reflect the spectral information and the entanglement characteristics of pixels in multispectral images. Therefore, an iterative self-organizing pixel matrix entanglement (ISOPME) image classification model is proposed in this article. Quantum pixel matrix entanglement (PME) is based on quantum pixel entanglement, which considers a pixel as a quantum, and the quantum entanglement theory in quantum informatics is applied in the PME. First, the PME theory was developed to associate the quantum states of pixels with their gray values. Second, the PME coefficient was proposed to extract the entanglement relationship between pixel matrices with 3-D basis vectors in the Hilbert space. Finally, an ISOPME model was developed to implement a self-organizing clustering for multispectral remote sensing image classification. The experimental results for four test areas demonstrate that: 1) the proposed ISOPME approach achieves an average classification accuracy of 92.02% and a Kappa coefficient (KC) of 0.88; 2) when compared with four traditional unsupervised classification methods, ISOPME on average improves the classification accuracy by 8.96% and the KC by 0.14; and 3) the classification accuracy and KC from ISOPME reach the same level as the more sophisticated supervised classification methods, such as support vector machine (SVM), and are close to those obtained using deep learning (DL) classification methods.

Existing photomultiplier tube (PMT)-based gating control systems embedded in bathymetric light detection and ranging (LiDAR) devices cannot automatically adjust the PMT gain in real time, resulting in the saturation distortion of echo signals or failure to receive echo signals. Therefore, this study proposes an adaptive adjustment method for the laser energy and PMT gain based on the self-feedback of echo data from a high-speed sampling and storage system. Zynq-7000 All Programmable SoC (ZYNQ) was used as the master controller, and the echo data analysis module, laser energy adjustment module, and PMT gain adjustment were designed based on the field-programmable gate array (FPGA). The corresponding control software was developed based on the ARM end to call the custom IP cores. The proposed method was experimentally validated using indoor corridors and outdoor water areas, such as pools, reservoirs, Wujiu Beach, and the Beibu Gulf. The experimental results demonstrated that the proposed method could adaptively adjust laser energy and PMT gain, with which the voltage amplitude fluctuation range of the echo signal was effectively reduced from the original -1.7 V–0 V (acquisition range of high-speed sampling and storage system) to -1.04 V– -0.26 V. With the comparative verification with multibeam sounder, the mean and standard deviation of the difference between the Z coordinates are -0.21 and 0.15, respectively, which indicates that the LiDAR using the proposed method achieves a similar accuracy of bathymetry.

Landslide natural disasters (LND) have high frequency, wide distribution, and multiple occurrences, causing significant losses to personal and property safety. LNDs account for over 70% of natural geological disasters in China, often caused by precipitation. Xingguo County, Jiangxi Province, is prone to LND due to its geographical location. Rainfall-induced LNDs account for over 70% of the county's LND. In this study, a digital modeling and machine learning approach is used to evaluate the susceptibility of rain-induced landslides in Xingguo County and generate a high-precision susceptibility map. Six influence factors are selected, and four machine learning algorithms, including support vector machine (SVM), decision tree (DT), back propagation neural network (BPNN), and random forests (RF), are used for susceptibility evaluation. A rainfall-induced landslide susceptibility map is derived, and landslide points are classified into five susceptive types. The experimental results show that the BPNN model achieved the best performance. The accuracy of the models is validated using the area under the receiver operating characteristic curve (ROC), area under the curve (AUC), accuracy (ACC), and kappa coefficient. The results showed that all models performed well, but the BPNN model achieved the best performance with an AUC of 0.75, ACC of 0.67, and kappa coefficient of 0.75.

Nowadays, Chinese power grid has developed very rapidly, and the transmission lines are massive. Our paper describes the use of an adaptive dynamic threshold algorithm and machine learning methods to detect wildfires in Yunnan province using Himawari-8. The algorithm extracts relevant features from the original NetCDF images and uses a dynamic threshold to identify wildfire pixels based on solar zenith angle and the proportion of cloud and non-vegetation pixels. Machine learning classifiers, including FCM+ SMOTE+SVM, are trained on the data using techniques to balance the dataset due to data imbalance. The improved classifier performs the best with a high accuracy for fire and non-fire pixels, outperforming other approaches including adaptive dynamic threshold, isolated forest, and one-class support vector machines. The FCM+SMOTE+SVM approach is shown to be robust for wildfire detection, but more data is needed to further improve its performance.

In order to explore the land deformation and its causes in the coastal areas of Guangxi Beibu Gulf, 36 Sentient-1A satellite images were used to obtain the surface subsidence rate and cumulative deformation of Guangxi Beibu Gulf from January 2018 to December 2020 based on PS-InSAR technology. Combining night-light remote sensing and rainfall data, we analyzed the causes of surface deformation. The results reveal: The deformation space within 4 km of the coastline of Beibu Bay and the urban areas of Fangchenggang, Qinzhou and Beihai were mainly in the port area of Fangchenggang, Qinnan District of Qinzhou and Haicheng District of Beihai, with the maximum accumulated settlement of -174.3 mm and the average deformation rate of -22.664 mm/a. The land subsidence rate in the Beibu Gulf area was affected by human activities and groundwater level, and the cumulative subsidence was positively correlated with the intensity of human activities. The rate of surface deformation decreases during the rainy season in July and August every year.

The modeling of the LiDAR underwater echo signal is mainly divided into analytical and statistical methods. In this paper, the radiation transfer equation of LiDAR underwater transfer is established according to the QSSA approximate analytical method theory. For Katsev, the Green function is directly used to solve the radiation transfer equation, and the spectral element method is proposed to calculate the LiDAR radiation flux in the radiation transfer equation. To verify the accuracy of the LS-SEM-Katsev analytical model, the simulation results of the LS-SEM-Katsev analytical model and Semi-MC statistical model are verified under three typical seawater optical parameters. The experimental results show that the decision coefficients of the three simulation models are all above 0.99, which is highly consistent. A comparison between the computational times of LS-SEM-Katsev and Semi-MC simulation models showed that the computational efficiency of the proposed analytical model was 8 orders of magnitude higher than that of the Semi-MC model.

Landslide is one of the major geological disasters in China, which brings huge economic losses to our people every year. However, in the field of landslide treatment, the application of machine learning is scarce. In order to fill the gap in the field of landslide treatment measures based on machine learning. Firstly, random forest classification or regression algorithm was used to train and forecast each landslide treatment measure in this paper. Accuracy (ACC) was used to test the model accuracy of classification algorithm, and Mean Absolute Error (MAE) is used to test the model accuracy of regression algorithm. Random forest classification algorithm was adopted for non-numerical measures. And random forest regression algorithm was adopted for the numerical treatment measures. Secondly, the feature importance of the random forest model was calculated to obtain the more important features of each landslide treatment measure in this paper. Based on this, an optimized random forest model was constructed, and finally the optimal random forest regression and classification algorithm model suitable for landslide treatment measures recommendation was obtained. The training data dimensions of the model were reduced from 58 dimensions to 4-10 dimensions. The experimental results showed that our model could greatly improve the accuracy.

Wildfire is a serious natural disaster that poses a serious threat to the safety of human life and property. Currently, there are many researches related to satellite wildfire detection, but few can achieve near real-time monitoring results. Himawari-8 geostationary satellite can provide full disk data every 10 minutes, making near real-time monitoring of wildfires possible. In this paper, a wildfire detection method based on Himawari-8 for multi-temporal data is proposed. In our method, we use temporal convolutional network (TCN) to predict the brightness temperature and achieve excellent prediction results, the mean absolute error (MAE) is 0.28 K, mean square error (MSE) is 0.30 K2, and mean absolute percentage error (MAPE) is 0.10 %. Then, the predicted values combined with other features as model inputs, and machine learning classification models were used for wildfire detection. The experimental results showed that the combination of multi-layer perceptron (MLP) model and strategy 2 containing brightness temperature predicted values achieved an accuracy of 90.91% in wildfire detection.

Shadow detection and compensation on high-resolution orthophoto is one of the most important tasks for ensuring high-quality of the radiometric balance of digital orthophoto map (DOM). This paper proposes a novel shadow detection method through semantic matching between the artificial “shadow” polygons (ASPs) and the real shadowed polygons (RSPs). The ASPs are created by digital building model (DBM), solar zenith and solar azimuth. A group of polygons semantic features, such as position similarity, area similarity, direction similarity and shape similarity are described and used as matching parameters, and then the real shadow regions (polygons) (RSPs) are detected using two-level matchings between the ASPs and the RSPs. The initial of matching aims at obtaining the initial probability and the candidates of the matching pair through determining the initial search circle cantered at the ASPs. The final of matching aims at finding the final match pairs through iteration of semantic matching, of which the maximum probability, which is calculated by the support coefficient of the adjacent match pair, is adopted as the criterion of the iteration. The experimental area located in Denver, Colorado, USA is used to validate the proposed algorithm. When compared with the dual-threshold, the Least-Squares Support Vector Machine (LS-SVM), and the UNet shadow detection method, the method proposed in this paper is able to increase the rate of shadow detection by 38.98%, 17.33%, and 13.14%, and decrease the fault detection rate by 19.43%, 10.01%, and 4.57%.

A traditional bathymetric LiDAR (light detection and ranging) has disadvantages such as large volume, heavy weight, necessity for airport and runway, and high cost for operation. For these reasons, this paper presents an off-axis four-reflection optical structure for single-band (532 nm) bathymetric LiDAR carried on UAV (Unmanned Aerial Vehicle). This optical system fully considers characteristics of the laser echo energy under different water conditions, which relate with the optical system parameters, such as peak power of laser emission, field of view (FOV), receiver aperture area, etc. The proposed optical system designs the objective lens, which are composed of one APD detector and two PMT detectors, the primary mirror, the second mirror, the plane mirror and the third mirror, two split field mirrors that separate the echo signals from shallow water, medium water and deep water, respectively. This proposed optical system was verified in laboratory tank, swimming pool, Lijiang River, lake, and the Qiaogang Sea Bay. It is found that the maximum water depth measured can reach 25.0 m with an error less than 0.1 m averagely. The dimension and weight of this LiDAR reach 90mm×160mm×90mm, and 10.25 kg, respectively, which is lightest and smallest bathymetric LiDAR worldwide.

Bathymetric light detection and ranging (LiDAR), which uses a laser with narrow pulse width and a high-repetition-rate, requires a real-time data acquisition system (RTDAQS) for quickly transmitting data with the characteristic of high sampling rates, high resolution and high accuracy. However, none of the RTDAQS in the world has reached these requirements. For this reason, this paper presents a RTDAQS with high speed SerDes data transfer technology based on the architecture of ADC+FPGA+ZYNQ. This RTDAQS consists of acquisition board and storage board. The acquisition board integrates high-speed analog-to-digital converter (ADC) acquisition module, Input/Output (I/O) control module, data transmission module, and Field Programmable Gate Array (FPGA) chip module. The storage board integrates FPGA Mezzanine Card (FMC) interface, solid state and ZYNQ chips. The corresponding software includes configuration for high-speed ADC acquisition module, I/O control module, data transmission module, and FMC interface module. The RTDAQS, which is embedded in the LiDAR with laser pulses width of 2.5ns ± 0.2ns and frequency of 500 Hz, has been verified by the experiments in indoor sink, outdoor pond, river, reservoir, Beihai Bay of the Pacific Ocean. The experimental results and comparison experiments demonstrate that the presented RTDAQS can reach a sampling rate of 2 GSPS and a sampling resolution of 14 bits and a sampling accuracy of 6 LSB with an acquisition error within 0.1 m, which meet the demands of high sampling rate with high accuracy data acquisition and quick data transmission in practice and can be applied to various devices.

Fractional vegetation cover (FVC) retrieval results of high spatial resolution satellite remote sensing images are usually upscaled as training and validation data (FVCUIH) for low spatial resolution satellite remote sensing images. However, few studies have focused on the impact of the spatial scale conversion on the evaluation of FVC retrieval accuracy. In this study, we first investigated the influence of spatial scale conversion on FVC retrieval accuracy based on FVC measured by unmanned aerial vehicle (FVCUAV) at three scales (Sentinel-2 MSI, Landsat-8 OLI, and MODIS). Then, the NDVI threshold method is proposed to further analyze the uncertainty caused by the underlying surface heterogeneity. The results showed that the use of FVCUIHas training and validation data in the process of spatial scale conversion led to overestimation of FVC accuracy, and its influence on FVC retrieval cannot be ignored. In addition, the uncertainty of the underlying surface heterogeneity at the measured sites increased the uncertainty of the FVC retrieval, while these results could be optimized by detecting the underlying surface heterogeneity. Our results suggested that both spatial scale conversion and underlying surface heterogeneity would cause the inaccurate FVC retrieval, while the latter could be optimized by detecting the underlying surface heterogeneity. This study provided a reference for the improvement of multiscale FVC retrieval accuracy based on single-scale FVC-measured data.

High-quality waveform decomposition, as one of the most critical cores of light detection and ranging (LiDAR) data processing, has become increasingly interesting. However, the current Gaussian decomposition method cannot handle the superimposed waveform with only one peak. Thus, this letter proposes a Gaussian inflection point selection method (GIPS). The method uses the number of inflection points (IPs) near the peak of the echo signal to judge the position of waveform half-width and selects an appropriate waveform half-width to iteratively decompose the echo signal to obtain Gaussian components, which are combined into a Gaussian model. Finally, a global Levenberg–Marquardt least-square algorithm (LM algorithm) is used to optimize the Gaussian model for fitting the echo signal. To verify the accuracy and effectiveness of GIPS, the experiments were conducted using land, vegetation and ice sensor (LVIS) data. The results show that the GIPS method can decompose complex LiDAR echo signals more correctly and efficiently than other methods do with an average$R^{2}$of 0.9799.

Clumping effect denotes the nonrandomness of foliage. It deviates from the random distribution assumption of Beer’s law which is usually applied to leaf area index (LAI) retrieval from large-footprint full-waveform light detection and ranging (LiDAR). Some studies correct for large gaps-induced between-crown clumping, yet ignore the within-crown clumping. The error of LAI caused by these clumping effects and the influence of the forest structure parameters on them have not been quantitatively studied. This study quantified the between-crown, within-crown, and total clumping indices through a theoretical derivation, clarifying the mechanism of clumping; we used airborne LiDAR point clouds data in 11 290 footprints (diameter = 25 m) to estimate these indices in real forests. We found that: 1) the underestimation of LAI caused by directly applying Beer’s law could be up to 93%, and it decreases with fractional crown coverage but increases with crown length and leaf area density; 2) the method of correcting between-crown clumping improves LAI retrieval for cylindrical canopies effectively; however, 3) considerable underestimation (up to 58%) exists if we neglect the within-crown clumping for other canopies, which has not been realized before; and 4) both the between-crown and the within-crown clumping can be the dominant contributor, and the within-crown clumping was greater than the between-crown clumping in 47% of the studied footprints. In the two physically based LAI retrieval methods, Beer’s law has been commonly used due to its simplicity. Pathways to improve future LAI retrieval would be instrument improvement to capture the between-crown gaps and method study to correct the within-crown clumping further.

The orthorectification accuracy for an extra-length linear array image is restricted by large distortions of the lens, the strong correlation of exterior orientation parameters (EOPs). This paper presents an orthorectification model for solving the above problems through dividing the distortion into two zones and modeling the distortions using two different models, as well as two constrains. The first one is the axial vector rotation coplanar, i.e., the axial vector is located in the imaging plane after two rotations, and the second one is the angle consistent, i.e., the angle between the ground space viewing vectors is equal to the angle between the ideal image space viewing vectors. The Beijing-2(BJ2) satellite images with a linear array of 30,076 pixels and the SPOT- HRV with 6000 pixels are used for verification of our method. The experimental results demonstrate that the orthorectification accuracy of the BJ2 image is improved from 1.938m (about 2.42 pixel) to 1.440m (about 1.80 pixel) and the orthorectification accuracy of the SPOT image is improved from 14.668m (about 1.47 pixel) to 8.657m (about 0.87 pixel). Thus, it can be concluded that the proposed method can achieve a higher accuracy than the traditional orthorectification method does.

In recent years, several models based on fully convolutional neural networks have been proposed. These models mainly focused on improving accuracy but ignored computational efficiency. For this reason, this research proposes an innovative deep learning model, entitled “ECA-MobileNetV3(large)+Seg-Net model” for simultaneously concerning both. In the encoder, the structure-reduced MobileNetV3(large) is selected as the backbone network, which uses 11 network layers to replace 20 network layers of MobileNetV3(large). The hybrid dilated convolution with dilation rates of 1, 2, and 4 is introduced in the depthwise separable convolution to expand the local receptive field and to enhance the connection of contextual information. Finally, the ECA module is fused with the bneck structure. In the decoder, the 18 network layers of SegNet’s decoder are replaced with 9 layers to achieve a lightweight network with small parameters. When compared with the original SegNet, the overall accuracy (OA) of the model proposed in this research is averagely improved by 5.97%, 5.56%, and 4.13%, and the sugarcane identification accuracy is averagely improved by 7.16%, 4.01%, and 9.13%, respectively in the three tested areas. Additionally, the memory size, the number of parameters, and FLOPs are all reduced by 6/7.

Classification of remotely sensed images (RSIs) is prerequisite for further applications. Of the existing RSI classification algorithms, the self-organizing mapping (SOM) neural network is considered as one of the most effective unsupervised classification algorithms. However, the traditional SOM only considers the Euclidean distance between neurons, which cannot reflect the correlation characteristics of neurons. For this reason, a radical algorithm, called “pixel entanglement (PE) through self-organizing pixel entanglement neural network (SOPENN)” is proposed in this article. First, the pixels in an RSI are considered as called “quantum pixels,” and all of the pixels in a RSI are considered as called “quantum pixel array”; then PE coefficient is proposed to mine the quantum entanglement relationship of quantum pixels on the array space; finally, a self-organizing neural network (SONN) is established to stimulate the PE behavior between quantum pixels. The theory mentioned above is applied in RSI classification to verify the performance of the SOPENN through four study areas. The experimental results demonstrate that: 1) the classification accuracy of SOPENN model proposed in this article averagely increases 8.42% when compared with the traditional unsupervised classification methods, Iterative Selforganizing Data Analysis (ISODATA), K-means, and SOM; and Kappa coefficient (KC) averagely increases 0.14; and 2) the classification accuracy and KC from the proposed SOPENN model reached the same level when compared with the supervised classification (support vector machine (SVM) model) in four study areas.

Landslides are common natural disasters that often cause serious impact and damage to human society. Since landslide disasters threaten people's production and life all the time, it is particularly important to predict the risk of landslides and to control landslide disasters. When studying landslide risk and deciding whether to treat the landslide, it is meaningful to classify and compare the risk of landslides so as to select those landslides with a higher degree of danger for priority treatment. The target of this paper is to extract factors related to landslide risk, and train a classification models for landslide risk. It employs ensemble machine learning algorithms to classify landslide hazards. Because the landslide feature has a large number of dimensions, this paper uses the PCA method to reduce the dimension. Due to the imbalance of the samples, this paper uses the SMOTE method to handle the imbalanced learning. The results of study show that the selected factors are highly related to landslide risk, the classification model in this paper has good accuracy.

LiDAR is now widely used in measurement. In view of the current problem that the Gaussian decomposition method decomposes the full-waveform LiDAR superimposed signal incorrectly, this paper improves the Gaussian inflection point matching algorithm to decompose the LiDAR echo signal. First, it is necessary to find the inflection point corresponding to the echo signal. Secondly, the inflection point and peak value are used to match to find the main waveform. Finally, the iterative method is used to find the remaining parameters and bring them into the LM(Levenberg-Marquardt) algorithm for parameter optimization. By decomposing the echo signals in the two regions of Antarctica and the United States, the method in this paper decomposes the echo signals in the Antarctic region with a success rate of 99.9% and an average R2of 0.9661. The success rate of decomposing echo signals in the United States is 88.7% and an average R2is 0.8833.

With the vigorous advocacy of the concept of green development, the protection and management of natural resources become more and more important. It is of great significance to study the classification of surface natural resources by remote sensing. In this paper, a high-resolution net (HR-Net) model is used to classify surface natural resources by Gaofen-1 (GF-1) satellite images and digital elevation model (DEM) data. First of all, we obtained the GF-1 satellite images, DEM data and census data of geographical conditions of the study area. And the first two kinds of data are integrated into five channels, which are red (R), green (G), blue (B), near infrared (NIR) and elevation channels. Second, we chose an area to make the labeled image with several classes contain surface natural resources. Third, we cut the image into training images and testing images, and the training images were made into 5000 images, 128 × 128 pixels to train the HR-Net model. Also for comparison, the experiment was carried out used the image without DEM data. Finally, we compared the accuracy, and our results showed that HR-Net model is useful and the image with DEM data has the better accuracy. Therefore, HR-Net and DEM data can be applied in practice to support the classification of surface natural resources.

At present, there are many deep learning models in the identification field, but they usually have problems with identification accuracy and slow speed. In this paper, we propose the S-MobileNetV2+SegNet model to identify sugarcane, and improve the SegNet model by replacing the encoder VGG16 model with the reduced structure Mobile-NetV2 model and introducing different ratios of dilated convolution to expand the local receptive field to eliminate the problem of insufficient information capture. Then reduce the network structure of the decoder and the number of convolution kernels to achieve a network with fewer parameters. To verify the accuracy and speed of the S-MobileNetV2+SegNet model in sugarcane identification, it is compared with SegNet, DeepLabV3+, and DeepLab-V3+Mobile-NetV2 models. The experimental results show that the S-MobileNetV2+SegNet model has better results and performance for sugarcane identification.

In recent years, the problem of land subsidence in urban areas has attracted more attention. differential interferometric synthetic aperture radar (D-InSAR) is a common surface deformation measurement technology. About our research, first of all, the processing effects of the ascending and descending images, VV polarization and VH polarization of Sentinel-1A data in the study area are compared. The ascending image with better coverage in the study area and VV polarization with better interference processing effect are selected. Second, the filtering algorithm and unwrapping algorithm in D-InSAR are contrasted. In terms of filtering algorithms, the improved Goldstein method with the coherence coefficient to adjust the power exponent of the weighting function in the frequency domain had the best filtering effect. In terms of unwrapping algorithms, there are fewer unwrapping islands in the minimum cost network flow method. Finally, D-InSAR is used to process the Sentinel-1A data to obtain the surface deformation results of downtown Chengdu. Therefore, the deformation of downtown Chengdu based on Sentinel-1A data is abtained.

In the airborne LiDAR system data acquisition task, it is necessary to use the calibration data for calibration, but there may be calibration failures or lack of calibration data. On the other hand, even after the corrected data, the features of the same name between the strip will have a large deviation. This paper proposes a multi-feature airborne LiDAR strip adjustment method combined with tensor voting algorithm for these two problems. First, use the tensor voting method (TVM) to calculate the plane feature intensity value of each point, set an appropriate threshold according to the plane feature intensity value to remove non-plane points, and use the CSF algorithm to remove ground points. Second, use the RANSAC algorithm to extract the building plane. Then, according to the Euclidean distance between the centroids of each plane point cloud, the adjacency of the planes is judged, and the intersection line of the adjacent planes is calculated. After that, the minimum Hausdorff distance (MHD) is used to determine the line pair. Then, rough registration is performed based on the average L1 distance on the set of minimized matching lines. Finally, the points on the plane are used to adjust the strip using the point-to-plane ICP algorithm. Proved by real data, the method proposed in this paper can have better results in data with large deviations between strip caused by correction failure, and the accuracy is slightly better than TerraMatch software. At the same time, it does not require any manual operation and auxiliary data or original data conversion.

Cameras equipped with fisheye lenses have large distortion, and conventional camera calibration methods are no longer applicable. In this paper, we considered the radial distortion, tangential distortion, and thin lenses distortion of fisheye camera. we take the equidistant projection imaging model of fisheye camera as an example to derive the three-point common line constraint model under equidistant projection, namely spatial resection model of fisheye camera. Firstly, the fisheye camera spatial resection model is derived, the initial values of fisheye image radius and image center coordinates are calculated by curve fitting method, then the intrinsic parameters of fisheye lenses are calculated accurately by least square method, and finally spatial intersection is carried out to calculate the ground control point coordinates for verification. Our method takes a short time, is simple and satisfies the requirements of real-time calibration.

In the research and development of the coastal ocean, the data of coastal water depth is very important. At present, the development of multispectral satellite water depth measurement is very rapid. In the actual water depth inversion, single-band method, double band method (ratio logarithm method), and multi-band method which are belonging to lyzenga's method, and Strumpf's logarithm ratio method, are widely used. But few people make a comparative analysis of these methods and study their differences. This paper uses the landsat8 oil data and multibeam depth data of Weizhou Island in Guangxi, preprocesses the oil data such as land water separation and atmospheric correction, and then retrieve the water depth by single band method, dual-band method(ratio logarithm method), multi-band method and Strumpf's logarithm ratio method, and uses standard deviation and R2 to evaluate the results of retrieving the water depth.

Wildfires may cause great casualties and heavy wildfires are becoming more and more frequently all over the world in recent years. However, due to the environmental limitation, high manual-dependent operation is often impractical with other limits. In this paper, a transfer learning neural network based on long short term memory (LSTM) was used to detect wildfire based on Himawari-8. The real time dynamic threshold value detection for cloud mask based on the modified Otsu algorithm was used to fast and accurately remove cloud areas where wildfire detection is failed due to signal blocking. Then, the experiments were conducted with LSTM and other models. The experimental results showed that our method was positive for wildfire detection.

The 3-D information collected from sample plots is significant for forest inventories. Terrestrial laser scanning (TLS) has been demonstrated to be an effective device in data acquisition of forest plots. Although TLS is able to achieve precise measurements, multiple scans are usually necessary to collect more detailed data, which generally requires more time in scan preparation and field data acquisition. In contrast, mobile laser scanning (MLS) is being increasingly utilized in mapping due to its mobility. However, the geometrical peculiarity of forests introduces challenges. In this article, a test backpack-based MLS system, i.e., backpack laser scanning (BLS), is designed for forest plot mapping without a global navigation satellite system/inertial measurement unit (GNSS-IMU) system. To achieve accurate matching, this article proposes to combine the line and point features for calculating transformation, in which the line feature is derived from trunk skeletons. Then, a scan-to-map matching strategy is proposed for correcting positional drift. Finally, this article evaluates the effectiveness and the mapping accuracy of the proposed method in forest sample plots. The experimental results indicate that the proposed method achieves accurate forest plot mapping using the BLS; meanwhile, compared to the existing methods, the proposed method utilizes the geometric attributes of the trees and reaches a lower mapping error, in which the mean errors and the root square mean errors for the horizontal/vertical direction in plots are less than 3 cm.

LiDAR footprint, defined as the illumination area of LiDAR sensor on the ground, is the fundamental unit that the sensor collects information from. The design of footprint size crucially influences the acquired LiDAR signals. For large-footprint full-waveform LiDAR, a well-designed footprint size is indispensable to acquire accurate and complete vertical profiles of scene targets. The methods that design the footprint size are increasingly needed to satisfy various application requirements. In this study, an analytical method to designing the footprint size is proposed for forest and topography applications. It is established based on a mixture Gaussian model and the designed footprint size ensures the signals of vegetation and ground can be completely extracted. Experiment results with our method show that the footprint size is preferably in the range of 10.6–25.0 m for forest application, while it is less than 32.3 m for topography application. The intersection of the two sets satisfies both applications. Furthermore, a series of sensibility studies were performed to analyze the influence of multiple key parameters to the optimal footprint size, including the scene characteristics, instrumental configurations, and application requirements. This study provides a theoretical basis for the design of future large-footprint full-waveform laser altimeters.

Oblique photogrammetry with multiple cameras onboard unmanned aerial vehicle (UAV) has been widely applied in the construction of photorealistic three-dimensional (3-D) urban models, but how to obtain the optimal building facade texture images (BFTIs) from the abundant oblique images has been a challenging problem. This article presents an optimization method for selection of BFTIs from the image flows acquired by five oblique cameras onboard UAV. The proposed method uses multiobjective functions, which consists of the smallest occlusion of the BFTI and the largest façade texture area, to select the optimal BFTIs. Geometric correction, color equalization, and texture repairment are also considered for correction of BFTI's distortions, uneven color, and occlusion by other objects such as trees. Visual C++ and OpenGL under the Windows Operating System are used to implement the proposed methods and algorithms. The proposed method is verified using 49 800 oblique images collected by five cameras onboard the Matrice 600 Pro (M600 Pro) UAV system over Dongguan Street, in the City of Ji'nan, Shandong, China. To restore the partially occluded textures, different thresholds and different sizes of windows are experimented, and a template window of $200\times200$ pixels2is recommended. With the proposed method, 2740 BFTIs are extracted from 49 800 oblique images. As compared with the Pix4Dmapper and Smart 3-D method, it can be concluded that the optimal texture can be selected from the image flow acquired by multiple cameras onboard UAV and the approximately 95% memory occupied by the original BFTIs is reduced.

Land use and cover change (LUCC) is a central component in current strategies for managing natural resources and monitoring environmental change. In this paper, we used maximum likelihood classification algorithm to obtain the supervised land use and cover classification. Four major land use and cover classes are identified and mapped from 2000 to 2015. The changes of land use and cover using Landsat images of the study area were analyzed. The results showed that: From 2005 to 2015, closed forest has increased and the annual rate of change was (+)3.3%. Open forest has an annual rate of change of (+)1.21%. Water bodies had an annual rate of change of (+)0.81%. While the settlements and bare lands had a decrease of 52.93 km2and the annual rate of change was (-)5.3%.

With the rapid development of urbanization, more and more attention has been paid to the structure of urban function zone. Thus, it is of great significance to investigate urban function zone. In this paper, we introduced the deep neural network (DNN) to infer the urban function zone with a supervised classification approach, taking the Shenzhen city in China as a case. First of all, the urban road networks of Shenzhen city were gathered and selected appropriately. Then, the fifth level road networks were utilized to segment the study region. Second, the communication data of different times and points of interest (POI) were collected. Then, the fifteen factors influencing urban function zone were derived. In addition, the urban function zone was divided into five types and the labeled examples with fifteen influencing factors were chosen. Third, the labeled examples were employed to train the DNN with different hidden layers compared with random forest (RF) and support vector machine (SVM). The models were trained with the approach of five-fold cross validation, and the average training accuracy with five times is taken as the accuracy of models. Finally, this paper compared the accuracy. It's been shown in the results that DNN was the optimum model and achieved the highest accuracy. Therefore, our proposed method is an efficient approach to infer the urban function zone.

Synthetic aperture radar (SAR) allows all-weather, day and night surveillance. Thus, it is of great significance for the ship detection and recognition. Because of the SAR special imaging mechanism, it is very difficult to extract the ship features with SAR image for the traditional target detection algorithm. In this paper, we proposed a approach which is composed of you only look once (YOLO) algorithm, sliding window detection strategy, and clustering algorithm. Firstly, the SAR images of GaoFen-3 and training dataset are gathered. Secondly, the experiments about the size of ship detection frame is carried out to find the optimum size of the frame for the training model. Thirdly, the ships are detected initially with YOLO v3 and fast region-based convolutional neural network (Fast-RCNN). Finally, the detected ships are clustered adaptively, and the experimental results of YOLO v3 and Fast-RCNN are compared and discussed at length. Our experimental results demonstrated that our method outperformed Fast-RCNN to detect the ships in the surface sea with low-resolution wide -band SAR images. Therefore, our approach is a robust method to detect the ships in the surface sea with SAR images.

Landslide disasters have caused incalculable losses to human. In China, 90% occurrence of landslides are directly induced by rainfall or indirectly related to rainfall. Because of its geographical location and the climate that belongs to the subtropical monsoon humid climate, the proportion of rainfall-induced landslides accounts for more than 70% of the total geological disasters in Bazhou district. In this paper, based on the geographic information system (GIS) technology, combined with the historical landslide hazard data, we conducted a study on landslide probabilistic quantitative model composed of landslide susceptibility evaluation and the rainfall intensity-duration threshold model. The research results showed that the prediction accuracy of rainfall model is 81.82%. And the landslide hazard prediction was carried out for rainfall-induced landslides and potential landslides, with a prediction accuracy of 90.91%. The research results showed that the meteorological early warning model results were consistent with the actual inspection results.

Drought is one of the main natural hazards affecting the environment and economy of countries all over the world. Fusing weather data with satellite images therefore becomes a superior method of identifying and monitoring drought in a given region. We established the relationship between land surface temperature (LST), the normalized differential vegetation index (NDVI) and rainfall data to derive areas of drought. Then, we obtained the indexes from the rainfall anomaly and NDVI anomaly as indicators which confirm the drought indicative claims of the maps produced. Our further examination of the NDVI, LST and rainfall maps indicate that the western, central and Volta Regions of the study area are the least prone to drought, with Axim (one of the most southern towns) in Ghana recording the highest rainfall in the country each year.

Global warming results in the rapid melting of glacial lakes in the Himalayas. In this paper, we took the Karakoram range in the Himalayas as the study area, and we derived the glacial lake area in 2001, 2014, and 2017 with semantic segmentation algorithm. Firstly, five high-resolution images in Mount Lucania were collected from Google Earth. Secondly, each image was labelled, and 80000 training images with the size of 256×256 pixels were derived using augmentation approach. Thirdly, a model of the stacking network of U-Net and SegNet based on these training images to extract the glacial lake was trained. Then, another five images of Karakoram range were derived from Google Earth as a testing dataset to demonstrate the performance of training model. Finally, the glacial lakes were extracted from the images of Karakoram range in the same month in 2001, 2014 and 2017. Our result showed that the area of the glacial lake in Karakoram range increased rapidly from 2001 to 2014, but decreased from 2014 to 2017.

This study aims to understand the effect of surface topography on vegetation high-order backscatterings of large-footprint full-waveform LiDAR. Most previous studies in exploring LiDAR scattering mechanisms were carried out over relatively flat areas. To explore the canopy scattering mechanism on slope terrain, this study implements the Discrete Anisotropic Radiative Transfer (DART) model to simulate the LiDAR single- and multiple-scattering waveforms in mountainous forest scene. Results show that 1) terrain slope changes the LiDAR scattering components at different time delays by changing spatial distribution of scene elements; 2) the ratio of multiple scattering to total intensity changes little with the terrain slope (less than 2%); 3) multiple scattering contributes most when the ground vertical extent is close to the canopy vertical extent. These findings may help in better understanding the canopy scattering processes on the slope terrain.

Due to the earth and moon's revolutions, the Doppler centroid of the moon-based synthetic aperture radar (moon-based SAR) varies dramatically along the orbit of the moon when the SAR system is looking broadside (nonsquinted). However, the Doppler centroid should be kept as small as possible to avoid range ambiguity and to alleviate difficulty with focus. This letter presents the Doppler properties along the orbit of the moon in accordance with the antenna coordinate system. Based on the Doppler analysis and the phase scan, we propose a method for Doppler centroid steering to minimize the Doppler centroid frequency without rotating the platform. The new method can accurately compensate the Doppler centroid to zero, because it considers the effects of the lunar orbit and the relative motion between earth's target and moon-based SAR. To validate the proposed method, we also derived the lower and upper bounds of the look angle. Subsequently, we performed simulations in accordance with Jet Propulsion Laboratory Development Ephemeris 430 (JPL DE430). Finally, the performance requirement of the phase scan is analyzed so as to validate the proposed method.

With the rapid development of sensors, the aerial photogrammetry brings us higher quality products, the detection and correction of building shadow area is also the key to improve the quality of aerial image. Therefore, this paper presents a method of building shadow determination based on DBM (digital building model). Firstly, a corner point on the shadow boundary is found by refining the edge from aerial image, and then the obtained coordinate of the corner point is combined with DBM to obtain the zenith angle of sun and the altitude angle of sun. Finally, the geometric relationship in DBM is used to determine the shadow on the roof of house (the roof obtained by orthorectification) and the ground from the ghost image. By using the method to detect the shadow of building, we can effectively avoid the problem: the previous method cannot detect the boundary of the shadow which is projected on the roof, and improving the speed of shadow detection.

Using the penetration of blue-green band (532 nm) laser in seawater, airborne laser scanning ranging technology can be applied to shallow sea measurements. The penetration properties of lasers due to the complex optical properties of seawater are also limited. The decrease of echo energy received by the sensor will greatly limit the accuracy of laser seabed survey. In response to this phenomenon, we propose a new structural design method combined with the optimal field of view (FOV) for shallow sea measurements to optimize the traditional receiving system to improve the laser receiving depth echo signal receiving capability. The simulation experiment is carried out by ZEMAX optical software. Experimental results show that the improved receiving system can effectively reduce spherical aberration and coma caused by spherical lens, and improve the imaging quality of the receiving system and the measurement accuracy of the shallow seabed.

The land resource is becoming scarcer and scarcer for a rapidly developing city. Thus, the land price assessment is important for the government to auction the land appropriately. In the paper, we introduced the deep neural network to evaluate the land price, taking the Shenzhen city in China as a case. Firstly, twenty influencing factors and land price data were gathered. Then, Shenzhen city was segmented into many grids with a size of 300 × 300 m. Secondly, the land price of each grid was derived with Kriging approach based upon the samples of land price. And the twenty influencing factors was quantified. Thirdly, the land price data and influencing factors were partitioned into training and testing datasets with the ratio of 8:1, and the training data were utilized to train the deep neural network based on regression analysis and classification with different hidden layers. Finally, the results were analyzed, and the deep neural network with the highest accuracy was selected as the optimum model. Therefore, our proposed method is an efficient approach to evaluate the land price with deep neural network.

Hyperspectral image is usually composed of hundreds of bands rich of spatial and spectral information. And this is an advantage for the common remotely sensed data. Thus, the classification of hyperspectral image could be of great value. However, the dimensionality of hyperspectral image may lead to the curse of dimensionality phenomenon when it is directly used for land use classification or other applications, making it difficult to be utilized effectively. In this paper, we presented a novel classification framework with capsule network based on the spectral and spatial information of hyperspectral images. At first, we use principal components analysis (PCA) to reduce the dimensionalities of hyperspectral image. Then, we use the capsule network to classify hyperspectral image. Our experimental result showed the novel classification framework is more efficient than other six popular methods. Therefore, the capsule network method is robust for hyperspectral image classification.

In recent years, the big data industry chain has become more mature. Analyzing and managing cities by utilizing various big data in cities has become a hot research topic. Urban functional regions discovering is one of the important applications. The mainstream in urban functional regions discovering are probabilistic topic models, such as latent Dirichlet allocation (LDA) based topic model, which seeing the regions as documents and their functions are their topics. These methods require feature engineering by hand, which will construct features of limited expressiveness. To overcome these methods' shortcomings, we introduced a deep learning topic model called document neural autoregressive distribution estimation (DocNADE) into urban functional regions mining. And we did an experiment to test its effect. The experimental result shows that this DocNADE framework has achieved a considerable result in urban function inference compared with Dirichlet Multinomial Regression (DMR) based topic model which is a state of the art of urban functional regions discovering.

To realize the on-board flood monitoring of SAR image, this paper proposed a SAR flood image change detection algorithm and its FPGA-based hardware implementation. The algorithm combines logarithm ratio and wavlet based multiscale analysis to generate the difference map,and the change region is obtained by CFAR thresholding method. The hardware implementation environment is (Xilinx XC7VH580Tflg1155-2). experimental results confirm the ectiveness of the proposed method in the respect of accuracy; in terms of running speed, the 512*512 image hardware implementation based on multiscale wavelet analysis takes only 50ms, which is ten times faster than PC implementation. hence the hardware implementation of the proposed method has good performance in terms of running speed and change detection accuracy.

To automatically search for mosaic seamline of urban large-scale orthophoto, which should avoid buildings, trees and other visually obvious foreground objects, this paper proposes a novel method of searching for mosaic seamline automatically based on an improved adaptive ant colony algorithm. The method based on ant colony algorithm and combined with the thought of Max-Min Ant System, avoids blind search in early phase and premature convergence phenomenon in later period, and improves the ability of global searching for optimal mosaic seamline. Experiment shows that the algorithm can only guarantee the quality of mosaic line, and has a great efficiency, and realizes the large-scale orthophoto intelligent seamless mosaicking.

Development of a city cultivates regions with different functions such as working areas and entertainment venues. People in a city usually travel among these regions in certain movement patterns. Identifying those regions will facilitate government management and promote further development of the city. In this paper, we proposed a framework to identify urban functional regions in Chengdu city based upon mobility pattern and point of interest (POIs) information extracted from mobile check-ins data. Firstly, unlike GPS trajectories, location check-ins were discontinuous. Thus, the typical mobility patterns of location check-ins was mined. Secondly, an arrival/departure matrix based on the typical mobility patterns was constructed to obtain the topics of regions by clustering POIs. Because we considered a region's function as our topics, we transferred the problem into a topic modeling problem, and applied an improved probabilistic topic model to infer functions of the regions. We evaluated our approach with 227,428 check-ins in Chengdu collected from Sina Weibo from April 12 2012 to February 16 2013. The results showed that our method outperformed baseline methods solely clustering POIs.

In order to solve the problem of risk assessment for mountainous geological disaster in southwest of China, we selected Wenchuan county as the study area, where the geological disasters happen frequently. the digital elevation model (DEM), and other geographic data of Wenchuan county were utilized to evaluate the risk of geological disasters. Firstly, the weights of factors for geological hazard susceptibility were identified using analytic hierarchy process (AHP). Secondly, the fuzzy distinguish matrix based on the strength of membership function was established by combining AHP with fuzzy comprehensive evaluation (FCE). Thirdly, the geological hazard risk system was constructed according to the elevation, slope, distance from the river or the fault zone. Finally, we divided the study area into three risk types: high, moderate, and low. Our research results showed that the landslide numbers of high, moderate, and low risk are 11759, 16889, and 6075, respectively, and the corresponding percentages of area in Wenchuan county are 34%, 49%, 17%, respectively. Our results were in line with the historical disaster data. Therefore, the governments should pay more attentions to the geological disasters of these towns.

In the field of remote sensing, data acquired from a single sensor usually can't meet the needs of some special applications, because the information extracted from the data are often incomplete and limited. Data fusing can solve this problem, but it will lead to the redundant information. In this paper, we proposed a novel manifold learning approach to perform dimensionality reduction for the fusing optical and SAR data. And three typical manifold learning models, namely, ISOMAP, local linear embedding (LLE) and principle component analysis (PCA), were utilized to test the robustness of our method by comparing with the land cover classification results. Our experimental results showed that our proposed method obtained the best land cover classification results among these approaches for the fusing optical and SAR data.

Drought is a weather phenomenon widespread worldwide due to the water shortage or unbalance of supply and demand, and it's also one of the most serious natural disasters for human life and agricultural production. The middle reach of Yangtze river, one of China's most important grain producer, subjected to the sub-tropical monsoon climate, is prone to have droughts. This paper has practical implications as it build a model by depending on the normalized difference vegetation index (NDVI) and land surface temperature (LST) of moderate resolution imaging spectroradiometer (MODIS) between 2005 and 2009. Firstly, the 8-day LST and 16-day NDVI data, 8-day LST and 30-day NDVI data were utilized to construct the LST/NDVI feature space. Secondly, the temperature vegetation dryness index (TVDI) images of the reach were derived respectively. Thirdly, the temporal evolution and spatial variation of drought was analyzed. Finally, the results of two different years were compared to analyze the drought in the reach. Our study showed the drought in May was more severe than that in other months. Therefore, a severe drought event is more likely to happen in May in the middle reach of Yangtze river and more measures should be taken to alleviate the loss for the governments.

The fraction of photosynthetically active radiation (FPAR) is a key parameter in controlling mass and energy exchanges between vegetation and atmosphere. LiDAR data-derived canopy vertical structural information and hyperspectral image-derived vegetation spectral information can be considered as complementary for vegetation FPAR estimation. To the best of our knowledge, few studies have estimated vegetation FPAR by both LiDAR and hyperspectral data based on physical models. This letter aims to explore the ability of combining airborne LiDAR and hyperspectral data to retrieve maize FPAR based on the energy budget balance principle. First, canopy gap probability and openness were estimated from airborne LiDAR data. Next, canopy reflectance and soil background reflectance were retrieved from hyperspectral image. Then, we estimated maize FPAR based on the energy budget balance principle. Finally, model validity was assessed byin situdata and results showed the physical FPAR estimation model estimated maize FPAR accurately. These results indicated that the physical method proposed in this letter was efficient and reliable to estimate maize FPAR, and FPAR retrieval can benefit from the complementary nature of LiDAR-captured canopy structural information and hyperspectral-detected vegetation spectral characteristics.

At present, APD (Avalanche Photo Diode) arrays LIDAR (Light Detection and Ranging) has been broadly accepted as an important means to obtain 3D (Three Dimensional) data. A new method of a scalable adaptive N × N channel communication system is introduced in the paper, which is aimed at improving the transmission rate of APD array LIDAR data. This paper presents the research on multi-channel communication system based on FPGA (Field Programmable Gate Arrays) configuration, which can be realized multi-channel parallel data independent receiving and transmitting. The multi-channel transmission system not only improves the data bandwidth, but also improve the load capacity of the system. The feasibility of multi-channel communication is verified sufficiently in the paper.

The traditional ortho-rectification technique for remotely sensed imagery, which is performed on the basis of ground image processing platform, has been unable to meet the timeliness requirements. To solve this problem, this paper presents the research on ortho-rectification technique based on field programmable gate array (FPGA) platform, which can be implemented onboard and spaceborne for a real-time processing. Through comparing the correction accuracy and the consuming time of traditional ortho-rectification method with the proposed method based on FPGA, it is demonstrated that the proposed FPGA-based onboard orthorectification has great advantage over improving the image ortho-rectification speed and can reach the requirements of correction accuracy.

In this paper a new way of timing discrimination to measure the timing point of echo signal in pulsed laser rangefinder is presented. This method is an improvement program of high-pass filter scheme. With multiple differential processing of pulsed laser echo signal through high-pass filter circuit, the timing point of laser echo signal will multiple forward approximately. The final identification time and the echo signal which rise along the time interval of initial point will be reduced to the minimum degree, and improve the overall accuracy of the results for a pulsed laser rangefinder, which will eventually minimize walk error between the timing point of the echo signal with the actual beginning timing point of the echo signal and improve precision of the results overall pulse laser radar rangefinding. In addition, the laser echo signal with a small distortion can be still measured and obtained the starting point of the leading edge. Finally, the experimental results show that the proposed new method can reduce the walk error caused by the time delay of the echo pulse timing discrimination, and can also deal with the distorted signal effectively.

The lithosphere is the most stored reservoir of carbon on Earth. The study of the effects of rock weathering and in the atmosphere provides an important basis for accurate prediction of CO2in the atmosphere, which is essential for predicting global climate change. One of the main factors that has been neglected in the global carbon cycle study of carbonate weathering carbon sinks is the rate problem. In this paper, some published data were used to study the influence factors of karst carbon sink and the establishment of corrosion velocity model. Based on the data of 18 corrosion test sites in China, a corrosion rate model was fitted, and then the data predicted by the model were compared with the data provided paper of Liu Rui, which are close to the measured data. The validated model can provide a research method for the carbonate certification rate in different regions, and solve the problems that cannot be tested because of time and space constraints. At last, net sink of CO2in the atmosphere is calculated as 4.77×1014g·a−1(or 130 million tC·a−1) in the global surface carbonate certification, which is close to the datameasured by Liu Zaihua and Ichikuni.

To implement on-board detection and matching of feature points on satellite, a FPGA-based hardware architecture of prototype was proposed in this paper. The SURF detector and the BRIEF descriptor are implemented on the selected FPGA (Xilinx K7 XC7K325T-1ffg900). Experiment results indicated that the detection speed of one frame with size of 256 × 256 was 1.5 ms under the clock frequency was 100MHz and most point pairs were matching correctly. In addition, the maximum utilization of resources was about 22%. Hence, the proposed prototype is good performance on detection and matching of remotely sensing imagery.

Since conventional decision tree (DT) induction methods cannot efficiently take advantage of geospatial knowledge in the classification of remotely sensed imagery, a co-location decision tree (CL-DT) method, combining the co-location technique with the conventional DT method, has been proposed by several researchers. However, the CL-DT method only considers the Euclidean distance of neighborhood events, which cannot truly reflect the co-location relationship between instances for which there is a nonlinear distribution in a high-dimensional space. For this reason, this paper develops the theory and method for a maximum variance unfolding (MVU)-based CL-DT method (known as MVU-based CL-DT). The presented method has been validated by classifying remote sensing image and is compared with CL-DT method and random forest (RF) method. The experimental results show that the proposed method can better construct decision tree and reach a high classification accuracy.

This paper proposes a novel occlusion detection method for urban true orthophoto generation. In this new method, occlusion detection is performed using a ghost image; this method is therefore considerably different from the traditional Z-buffer method, in which occlusion detection is performed during the generation of a true orthophoto (to avoid ghost image occurrence). In the proposed method, a model is first established that describes the relationship between each ghost image and the boundary of the corresponding building occlusion, and then an algorithm is applied to identify the occluded areas in the ghost images using the building displacements. This theory has not previously been applied in true orthophoto generation. The experimental results demonstrate that the method proposed in this paper is capable of effectively avoiding pseudo-occlusion detection, with a success rate of 99.2%, and offers improved occlusion detection accuracy compared with the traditional Z-buffer detection method. The advantage of this method is that it avoids the shortcoming of performing occlusion detection and true orthophoto generation simultaneously, which results in false visibility and false occlusions; instead, the proposed method detects occlusions from ghost images and therefore provides simple and effective true orthophoto generation.

True orthoimage (TOM) was proposed to solve the problems that DOM generation encountered with high resolution images in urban areas, especially in the areas with high buildings. The correct positions of roof texture are located with the detection of the occlusion and shadows. However, the wall texture is missing after true orthoimage generation. Thus, in this paper, we proposed a method for automatically texture extraction, mapping and 3D visualization of buildings facades based on high resolution aerial photos. There are four major steps: 1) Visibility judgment based on occlusion detection and angle; 2) Optimal texture source selection; 3) Real texture extraction and mapping of building facades; 4) 3D visualization with real texture of building facades. This research can largely reduce workload and provides reference and guidance for applying high resolution images to generate real 3D urban reconstruction.

Surface soil moisture (SSM) is an important variable in environmental studies and land surface system research. Remote sensing techniques provide a direct and convenient means to estimate SWC on a regional scale. Land surface temperature (LST) and vegetation index (VI) can be employed to construct a feature space that represents surface dry and wet conditions. A normalized soil water content model was developed to obtain comparable SWC using normalized LST (T*) and VI. The quantitative relationship among SWC, LST and VI was shown by a quadratic polynomial equation. In this study, a uncertainty sensitivity analysis for the input parameters and other factors that might affect the established model was conducted. T* and Fractional vegetation cover (FVC) are key inputs, and the uncertainties introduced by them are necessarily required for the model. The results showed that LST affects the estimation results enormously. The estimation error is approximately 0.05 m3/m3when the LST with 1K uncertainties. And the FVC has a weak influence on the soil moisture estimation. The estimation error will be 0.03 m3/m3when the FVC has an error of 0.2. For targeted analysis, LST changes from -0.2k to 0.2K and FVC has an error of 0.2. The largest error of estimation can reach 0.03 m3/m3when the two variables have the biggest uncertainties.

Traditional ortho-rectification is the most commonly method, which used to rectify the deformation caused by center-projection. However, the occlusion caused by high buildings could not be recognized by computer in the process of traditional ortho-rectification, and the double-mapping is raised. This is the so-called “ghost image”. In order to eliminating double-mapping and detecting occlusion, this paper proposes a new method to detect the occlusion caused by buildings' visual walls. The basic principle is establishing a model to describe the relationship between ghost image and occlusion including its size and shape/boundary, and then this paper applies seed growing method to detect the occluded area on ghost image according to the building's boundary. The experimental results demonstrate that the proposed method have a better accuracy and efficiency.

Under the indoor building environment, when the fires and other accidents occur, how to effectively organize the masses evacuation and fire rescue, is closely related to the safety of people's lives and property and has become a critical problem of public concern. This paper presents an improved ant colony algorithm (ACO) to solve the problem of how to optimize the evacuation route and rescue route when an accident occurs. According to the key factors affecting people emergency evacuation, such as indoor building environment, fire and its combustion products, problem of path's optimal selection, etc., we propose an emergency evacuation model, based on the model it can give an optimal evacuation route for the mass and an optimal rescue route for the firefighters. We also analyzes the search results, it shows that the search results is robust and reasonable.

Much attention was paid on monitoring of the subsidence along large-scale man-made linear features (LMLFs). The high resolution SAR data offers impressive details on these infrastructures with multi-temporal INSAR analysis. This paper validates the INSAR results of time series analysis along a certain railway in China using a high density leveling campaign. The Leveling points are distributed along the railway line and each two leveling points are 60 m apart at a time interval of nearly one month. The study area is located in the west of Tianjin downtown, covering an area of 1.5 km · 7.46 km. The validation results show that the average subsidence rate and time series of PS results are agreed well with that of these leveling points.

Texture extraction and mapping is a key step for 3D reconstruction. The major methods are depending on experiences, scene photograph, close-range photogrammetry and oblique photogrammetry. They all require collecting additional images, which it is costly, time consuming and laborious. Meantime, the texture information acquired in the original aerial photos with high resolution is lost. Then, it is more economical and closer to reality if we can recover texture information from original aerial photographs. Thus, this paper presents one texture extraction and mapping method, with consideration of occlusion detection. There are two major characteristics: 1) Based on visibility judgment, we add the occlusion detection, which avoid the false texture extraction when the current building texture is occluded by other buildings. 2) We realize the real texture extraction and mapping from original aerial images. The location of the texture in original images is calculated with the collinearity equation and rectified texture coordinates.

In China, drought is one of the major environmental disasters, which bring great harm to the people. The middle reach of Yangtze River is the most important base to produce grains in China. Influenced by the summer monsoon, the drought occurs frequently. In our paper, the NDVI and LST from MODIS data were utilized to calculate the TVDI (Temperature Vegetation Dryness Index), which were used to monitor the drought of the study area. Meteorological drought indices were calculated from 10-day precipitation, temperature and evaporation data of 94 meteorological stations, including precipitation standardized variables, dryness and relative moisture index were used to analyze the degree of drought and the area of drought. The results showed that TVDI is significantly related to soil moisture.

On-orbit geometric calibration is a critical and essential step to guarantee the high geometric positioning accuracy of high-resolution optical satellite imagery. In this paper, we first review and summarize the on-orbit geometric calibration methods for high-resolution optical satellite and then analyze their advantages and disadvantages. Finally, we present our perspective on on-board geometric calibration which can be implemented automatically in real time. With the overview of geometric calibration developed in the past decades, the two conclusions could be driven up: (1) the current on-orbit geometric calibration technology based on ground control points (GCPs) has been mature, which can largely improve the geometric positioning accuracy of satellite imagery; (2) new innovation method of on-board geometric calibration for real-time improvement of satellite imagery positioning accuracy is needed. In the end, this paper presents our technique frame of on-board geometric calibration system.

At present, APD (Avalanche Photo Diode) arrays laser imaging based-on Geiger-mode has been a main research of laser radar. For low-cost and small APD arrays, we propose a distance imaging method based on APD working at Linear-mode. For this reason, the prototype of GLiDAR-II has been developed. The test shows that the max imaging range is 20 meters and the precision is better than 5 cm. This paper first introduces the principle of the prototype, its hardware framework and the principle of 3D imaging program, then focuses on the data communication, data correcting and the analysis of the real-time imaging results. Finally, in order to check the 3D image details of historical data conveniently, a program has be written, which can set historical data range, data type, scanning direction and imaging color. The results of historical imaging will be analyzed and discussed in the end.

This paper presents a FPGA (Field Programmable Gate Array)-based remotely sensed imagery denoising method, named FPGA-based median filtering. The proposed method is capable of processing large volume data since it takes full advantages of FPGA hardware and abundant logic units. This paper first overviews the traditional median filtering algorithm, and then highlights the FPGA-based filtering, including hardware components, software platform, and implementation of traditional median filtering using FPGA hardware in detail. The image frame with two dimensions of 10000×10000 pixels2is employed to test the proposed method. The comparison analysis between the proposed FPGA-based median filtering method and traditional median filtering based on ENVI version 4.8 software is carried out. The experimental result demonstrates that the proposed method is capable of saving the time more than 20 times than traditional median filtering based on ENVI version4.8 software does.

Retrievals of surface soil moisture (SSM) from remotely sensed satellites have become important in agriculture, meteorology. However, any models have been presented for different satellite imagery, which are complex with data processing cumbersome and inconvenient. This paper introduces the one model of CBERS-02B and two models of Landsat TM image. Comparing and analyzing the accuracy of the models from CBERS-02B satellite imagery. The results showed that the overall accuracy of SSM from CBERS-02B's average soil moisture content reached 91.26%. It is higher than the accuracy of the other models of Landsat TM. These results demonstrate that the model can effectively calculate the SSMs for CBERS-02B satellite imagery.

Rocky desertification is a type of land desertification. It comes from the fragile ecological and geological environment, where the human activity is very strong and the land productivity is degraded severely. As a natural disaster, rocky desertification is very destructive, and it is very difficult to be recovered. The karst region of China in southwest is the world's concentrated karsts region. It is also one of the largest contiguous karsts regions. The karst region is also the most typical ecological fragile regions in China. We utilized the ETM+ images in 2000 to study the rocky desertification of the north regions in Guangxi province in the past ten years. Firstly, the rocky exponential model was established to extract rocky desertification information of the region. Then, the RGB image was composited to interpret and obtain the rocky desertification. Our experiment showed that rocky desertification of the karst region can be classified into no rocky desertification, moderate desertification, and severe rocky desertification.

The karst collapse is one of the six major geological disasters in China, the karst region is also the main geological disasters, to the integration of subsidence data management and evaluation is particularly important. In this paper, Lai bin, Jili of Guangxi province is taken as an example, combining RS and GIS technology for thematic information extraction and data management, extracted from remote sensing image subsided land use types; Use of GIS software ArcGIS for platform, combining with subsidence history text, drawings, and on-the-spot field to get the data, established the perfect combination of spatial database, graphic data of subsidence geological environment evaluation and spatial analysis provides a scientific and effective management; The last, The use of GIS space analysis function to completed accurate sensitivity assessment collapse rapid.

This paper develops an ecological index (EI) model to assess ecological environment, which is more suitable for Guilin Karst Scenery, because of highlight a greater weight in land degradation, and a lower weight in biological abundance. The EI model considers the biological abundance index, vegetation coverage index, water density index, land degradation index, and environmental quality index, and these indexes are 55.7, 62.7, 69.8, 25.7, and 93, respectively. These indexes are taken into the EI model, and the ecological index is 60.3, which means that the karst ecological environment reaches a “Good” level, but not reaches an “Excellent” level. The results show that the City of Guilin, China presents a higher coverage of the vegetation, a richer biological diversity, and a suitable environment for human being survival. However, the results also indicate that the environmental problems have been arisen at the same time.

This article extracts and analyzes disaster information by using GIS and RS technology. It determines the weights by using the Analytic Hierarchy Process (AHP), and it constructs risk evaluation model to do risk evaluation of geological disasters. In this paper, Lai bin, Jili of Guangxi Province is selected as an example to evaluate the risk of Karst collapse because of its strong karst landform. This paper obtains karst collapse geological hazards risk zoning by using GIS and RS technology for the first time. Through analyzing the geological environment in the study area, it selects 10 evaluation factors to evaluate the risk of Karst collapse. At last, it gets the zoning evaluation map. The evaluation result is consistent with the actual situation in the study area, and it provides reference data to the economic development as well as for the prevention of karst collapse.

A new decision tree induction method, called co-location-based decision tree (CL-DT), is presented in this paper to extract exposed carbonatite in karst rocky desertification area. The proposed algorithm utilizes co-location characteristics of spatial attribute. This paper first presented the co-location mining algorithm, including neighbor graph construction, determination of distinct event-type, pruning non-prevalent co-locations, and inducing co-location rules, and then focused on developing the algorithm of co-location decision tree, which including non-spatial attributes data and spatial data selection, co-location modeling, node merging criteria, and co-location decision tree induction. This paper uses Landsat-5 TM images covering the whole Du'an city in China as the data to verify the proposed method. The experimental results demonstrated that (1) Compared to traditional decision tree, the proposed co-location decision tree has higher accuracy and can make better decision; (2) The training data can be fully played roles in contribution to decision tree induction.

Although many efforts have been made on the fusion of Light Detection and Ranging (LiDAR) and aerial imagery for the extraction of houses, little research on taking advantage of a building's geometric features, properties, and structures for assisting the further fusion of the two types of data has been made. For this reason, this paper develops a seamless fusion between LiDAR and aerial imagery on the basis of aspect graphs, which utilize the features of houses, such as geometry, structures, and shapes. First, 3-D primitives, standing for houses, are chosen, and their projections are represented by the aspects. A hierarchical aspect graph is then constructed using aerial image processing in combination with the results of LiDAR data processing. In the aspect graph, the note represents the face aspect and the arc is described by attributes obtained by the formulated coding regulations, and the coregistration between the aspect and LiDAR data is implemented. As a consequence, the aspects and/or the aspect graph are interpreted for the extraction of houses, and then the houses are fitted using a planar equation for creating a digital building model (DBM). The experimental field, which is located in Wytheville, VA, is used to evaluate the proposed method. The experimental results demonstrated that the proposed method is capable of effectively extracting houses at a successful rate of 93%, as compared with another method, which is 82% effective when LiDAR spacing is approximately 7.3 by 7.3 ft2. The accuracy of 3-D DBM is higher than the method using only single LiDAR data.

Considering a poor condition in using traditional instruments to monitor sea state near Beibu Gulf, this paper presents an economic, flexible, low cost and high accuracy GPS buoy which is mainly used to verify wave parameters which are produced from SAR image inversion. Tide information is removed by wavelet transform, and wind wave information is used to calculate spectrum by Fast Fourier Transform (FFT). In the experiment, GPS buoy presents a flexible and low cost performance in monitoring wind wave. The results show that calculated wave parameters can reach a satisfied and acceptable accuracy and can be used in verifying the outcomes which are calculated by the SAR.

It is a robust and efficient method that utilizes morphological operators, and Otsu threshold together with GIS road vector to detect vehicles from high resolution highway aerial images. Our previous experiments showed that the method resulted in a high correctness, completeness, and quality. However, we did not discuss the relation between the accuracy and the size of structure element for vehicle detection with high resolution highway aerial images. The space resolution of our aerial images is 0.15×0.15m, and most vehicles' size is normally 5×2m, approaching 34×14 pixels. Thus, we selected a disc as structure element, but the disc's radius should be less than 7 pixels. We compared the vehicle detection accuracy with 3 pixels, 4 pixels, 5 pixels, 6 pixels, and 7 pixels of disc with different size. Our result showed that the radius with 5 pixels or 6 pixels obtained the highest accuracy.

Most of the surface soil moisture (SSM) models developed in recent decades are not for karst area where the surface soil is thin. Consequently, this paper presents a novel algorithm for retrieval of the SSM on the basis of “Optical Vegetation Coverage” for CBERS-02B imagery. Jili village in a typical karst area is chosen as the study area, and the retrieved SSM by Landsat TM satellite imagery is chosen for evaluating the accuracy of the proposed model. It shows that the relative accuracy of the mean SSM overall reachs up to 91.26%, and the correlation coefficient R is up to 0.8. Moreover, the R of the rocky desertification land and the dry land even reaches more than 0.9. With these experimental results, it can be demonstrated that the proposed model based on CBERS-02B satellite imagery has a high precision in the retrieval of SSM in karst area.

Vehicle detection from high resolution aerial images has been studied for many years. However, a robust and efficient vehicle detection is still challenging. In this paper, a novel and robust method for automatic vehicle detection from aerial images was presented. In this method, a GIS road vector map is used to constrain a vehicle detection system to parking lot networks, edge detection and morphological preprocessing method are used to identify candidate vehicle pixels. Different types of vehicle templates are selected to adaptively detect the similar vehicles by their correlation coefficient with the same size of the window. Experiment was conducted using 0.15 meter resolution aerial images, the result demonstrated that the new method had an excellent detection performance.

Inversing the surface wave spectrum from SAR (Synthetic Aperture Radar) is widely used to obtain the ocean wave parameters in a large ocean area. Considering that the ocean wave is affected by the water depth factor in coastalarea, this study focuses on the inversion of wave parameters in shallow ocean area from SAR images, and using the shallow water TMA spectrum as the first-guess spectrum to develop the inversion method presented by Hasselmann. The surface wave spectrum under swell-dominated case can be inversed from SAR image spectrum directly.So this paper selects 10 sub-images of one ERS-2 image which locates in Zhanjiang city sea area where under the wind-wave dominated cases. The wave lengths and wave directions are calculated from the inversion wave spectrums.Radon transform method is used to compare results. The result indicates that the wave lengths calculated by this inversion method and the radon transform agree with the bias of 4.07m, the standard deviation of 5.05m and the correlation coefficient of 0.953; the wave directions calculated by this inversion method and the radon transform agree with the bias of 1.93°; the standard deviation of 2.70°and the correlation coefficient of 0.955. When the water depth decreases, the change of wave length calculated by this inversion method is consistent with the change of ocean wave length.

The method for retrieving the ocean wave spectrum from the Synthetic Aperture Radar (SAR) imagery is currently the most widely used for calculation of the wave high, wavelength and wave direction. However the most current of methods are relatively complicated. This paper presents an algorithm which is based on Radon transform. In this algorithm, firstly, with the property of Radon transform that the line in the SAR image and the point in the transformed space are one-to-one correspondence, the texture feature of the wave in SAR image is detected to calculate the wavelength and probable wave direction. Secondly, according to the theory of the ocean wave and the trends of the wavelength in two close sub-images, the actual wave direct ion is determined eventually. This article selects an ERS - 2 SAR image around Taiwan as the study area. The results of calculation are compared with the results obtained by Two-Dimensional Fast Fourier Transform (2D- FFT). The study results demonstrate that the use of the Radon transform is available without loss of the accuracy when retrieving the wave wavelength and wave direction from the ERS-2 SAR images.

This paper presents the study of SAR imaging spectrum simulation using the shallow water TMA (Texel-Marsen-Arsloe) spectrum. The study area is located in Beibu Gulf of the South China Sea. The simulated study first selects coastal sea wave spectra with different water depths ranging from 10 m through 40 m to analyze the influence of water depth on SAR imaging spectrum. Furthermore, the change of SAR imaging spectrum is compared by changing the direction of waves propagation at 0°, 45° and 90°, wind speed at 10 m/s through 18 m/s and polarization modes with HH and VV at a certain water depth with an average water depth of 30m. The results discover when the water depth decreases, the SAR spectra peak moves to the high wavenumber region.

This paper presents a new generation of scannerless laser radar measurement system, called GLidar in our project. This system does not require the scanning device; as a result, the dimension of the entire measuring system is small, lightweight, and reliability. The proposed scannerless LiDAR system is especially designated for a small civil UAV platform under a low altitude operation. This paper presents the principle of the array LiDAR imaging, mathematical models of calculating the 3-D coordinates of the laser radar footprints with respect to a mapping coordinate system. Some simulated results are presented on the basis of a test field located in Virginia Wytheville, USA. The simulated results demonstrated that the designated new generation of array LiDAR can achieve 0.06–0.10 m in flat area, 0.31–0.62 m in the edge of house, and 1.23–1.78 m in the forested area compared to an existing DSM data.

This letter presents a method to calculate the width ω over the half-period of the cosine term in a diurnal temperature cycle (DTC) model. ω deduced from the thermal diffusion equation (TDE) is compared with ω obtained from solar geometry. The results demonstrate that ω deduced from the TDE describes the shape of the DTC model more adequately around sunrise and the time of maximum temperature than ω obtained from solar geometry. Additionally, taking into account the physical continuity of land surface temperature (LST) variation, a day-to-day temporal progression (DDTP) model of LST is developed to model several days of DTCs. The results indicate that the DDTP model fits in situ [or Spinning Enhanced Visible and Infrared Imager (SEVIRI)] LST well with a root-mean-square error (RMSE) less than 1 K. Compared with the DTC model, the DDTP model slightly increases the quality of LST fits around sunrise. Assuming that only six LST measurements corresponding to the NOAA/AVHRR and MODIS overpass times for each day are available, several days of DTCs can be predicted by the DDTP model with an RMSE less than 1.5 K.

In remote sensing and agriculture, corn is a common crop which is often studied. In both cases, it is important to measure the geometric structural parameters such as Leaf Area Index (LAI) and Leaf Angle Distribution (LAD). They are useful indicators that affect corn growth. Kinect is a sensor that can be used to get the distance between the object and Kinect itself. It costs little but offers high accuracy. We use Kinect to obtain point clouds of the corn and build a 3D model of the leaves in order to measure structural parameters. The current results show the proposed method is feasible. But more efforts should be made to improve the automation and practically of this method.

Vehicle detection from high resolution aerial images has been studied for many years. However, a robust and efficient vehicle detection method is still challenging. In this paper, a novel and robust method for automatic vehicle detection from highway aerial image was presented. In this method, a GIS road vector map is used to constrain a vehicle detection system to the highway networks. After the structure element is identified, morphological preprocessing method is used to identify candidate vehicles. Experiment is conducted with 0.15 m resolution aerial image. And the result demonstrated that the novel method has an excellent detection performance, thus the method is very promising.

It was noticed long time ago that Karst rocky desertification is gradually developed into the third-largest ecological problem. Remote sensing is mostly used in the research of Karst desertification. However, the previous research used original imagery, so it cannot effectively interpret the characteristic of rocky desertification. This paper chooses the first three components of the KT transform of ETM+ data to establish rocky desertification exponential model. Four variables: index of Karst (IK), brightness, greenness, and wetness were extracted to characterize the rocky desertification in the study. The last three variables are the first three components of the KT transform. Their interrelationships were examined using a correlation matrix. The investigated results indicate that index of Karst is mostly correlated with the brightness (r= 0.64), but negatively correlated with greenness (r= -0.81) and wetness (r= -0.51) (at 90% confidence level). These findings can be used to mitigate rocky desertification by city planners, environmental managers, and other policy makers for designing measures.

This paper presents a comparison analysis for methods of land use classification, between object-oriented and Maximum likelihood methods in Karst area. Nine types of objects are selected in terms of “Standard of Land Classification” (version 2001)” and “Classification of Land Use (version 2008)”, China for classification. They are Shrub land, dry land, construction land, bare land, woodland, irrigation land, water, forest land, and garden land. Comparison analysis of land uses using object-oriented and Maximum Likelihood Classification method is conducted. Downtown of Daxu county, Guilin, China in a typical karst area is chosen as the study area, and ISR-P6 satellite imagery are chosen for experiments. The experimental results discover that classification results have a significant impacts to land use classification in karst area. The object-oriented classifier achieved the classification accuracy of 93.96%, whereas the maximum likelihood classifier produced a classification accuracy of 78.94%.This study demonstrates that the object-oriented classifier is significantly better for classification of land use in karst area.

Airborne LiDAR data and optical imagery are two datasets used for 3D building reconstruction. The researchers have developed a variety of modeling method using these two kinds of data. In this paper, we firstly reconstructed the buildings in the test site using the above three kinds of data sources. And then, we compared the results quantitatively. We adopted the primitive-based building reconstruction method to reconstruct the buildings using the two types of data.

Detailed tree information, such as tree counts, tree heights, crown base heights, diameter at breast height (DBH), and tree biomass, is critical for the effective management and quantitative analysis of trees in urban area. Automatic detection of trees, and their parameters using light detection and ranging (LiDAR) data has been widely employed. However, at the single-tree level along road in urban area, LiDAR data deposed the disadvantages such as space points, no texture information, so that the detailed tree parameters mentioned above cannot successfully be obtain at enough accuracy. This paper presented an integration of LiDAR data and ground mobile truck data. This development is driven by the fact that the information obtained from ground-mobile truck images can be substantially complemented by the data from LiDAR.

This paper presents a method of geo-referencing the video data acquired by a small low-cost UAV, which is specifically designed as an economical, moderately functional, small airborne platform intended to meet the requirement for fast-response to time-critical events in many small private sectors or government agencies for the small areas of interest. The developed mathematical model for geo-locating video data can simultaneously solve the video camera's interior orientation parameter (IOP) (including lens distortion), and the exterior orientation parameters (EOPs) of each video frame. With the experimental data collected by the UAV at the established control field located in Picayune, Mississippi, the results reveal that the boresight matrix, describing the relationship between attitude sensor and video camera, in a low-cost UAV system will not be able to remain a constant. This result is inconsistent with the calibrated results from the previous airborne mapping system because the boresight matrix was usually assumed to be a constant over an entire mission in a traditional airborne mapping system. Thus, this paper suggests that the exterior orientation parameters of each video frame in a small low-cost UAV should be estimated individually. With the developed method, each video is geo-orthorectified and then mosaicked together to produce a 2-D planimetric mapping. The accuracy of the 2-D planimetric map can achieve 1-2 pixels, i.e., 1-2 m, when comparing with the 43 check points measured by differential GPS (DGPS) survey.

This paper first presents how to exam which type of errors causes this incomplete orthorectification in urban highbuilding area, and then presents a new method for orthorectification of high buildings in urban area. The proposed method in this paper is different from the traditional methods, which improved the accuracy by increasing the number and/or improved the geometric distribution of ground control points. This proposed method first established mathematical model of constrain condition on the building edges, such as perpendicularity, and then the established constrain conditions are merged into the orthorectification model. A test field has been used to evaluate our methods. The experiments of comparing the accuracy achieved by our method and other methods are conducted. The experimental results demonstrated that the proposed method can improve the accuracy of 2-5 feet for those buildings of over 100 m high.

This paper proposes an improved calibration method based on multi-view's vanishing point to estimate the accurate pose and location parameters of camera in building scene. This method put the lens distortion and orientation parameters of camera directly into the calibration mode. Furthermore, using the line feature existing in the building surface, geometric constraints are introduced into the calibrating model. The orientation parameters of camera can be estimated accurately by this method.

A method for real-time mosaic of video flow acquired by a small low-cost unmanned aerial vehicle (UAV) has been presented in this paper. The basic procedures of real-time mosaic are as follows: (1) Each video frame is resampled and orthorectified using a developed mathematical model, which can simultaneously solve the video camera's interior orientation parameters and the exterior orientation parameters of each video frame; (2) each orthorectified video frame is mosaicked at real time. A test field located in Picayune, Mississippi, has been established for testing our method. Sixty-minute video data were collected using the UAV and were processed using the proposed method. The results demonstrated that each video frame can be geo-orthorectified and mosaicked together to produce a 2-D planimetric mapping at near real time. Accuracy of the mosaicked video images (2-D planimetric map) is approximately 1-2 pixels, when compared to 55 checkpoints, which were measured by differential GPS surveying.

In recent years, many civil users have been interested in unmanned aerial vehicle (UAV) for a verity of different applications, such as quick response to time-critical event, traffic monitoring and traffic data collection because they have the ability to cover a large area, focus resources on the current problems, travel at higher speeds than ground vehicles, and are not restricted to traveling on the road network. All of these applications require a real-time orthorectification of the image flow acquired by UAV. This paper presents a method for real-time UAV orthovideo generation. The major contributions in this paper contain, video image matching to find tie points for merging dynamic, narrow field-of-view aerial video into a mosaic orthoimage, and a simple and fast algorithm for orthoimage. Finally, a real-time mosaic algorithm was developed.

The most common measurement of usage in road network is vehicular traffic flow, which is defined as the number of vehicles passing a specific location along a road during a unit of time. Flow is often expressed as AADT (the annual average daily traffic). Traditionally, the traffic vehicle flow is measured by using ground-based sensors, most common of which are inductive loop detectors and pneumatic tube detectors. The sampling rate of a detector depends on the application and the operating agency, typically ranging from under one minute to a full day. This paper presents methods for spatial measures and interpretation of roadway network from aerial remotely sensed image. First, this paper present the interpretation of roadway network using co-registration between up-to-date aerial imagery and geospatial database, which is called image-to-GIS co-registration. The up-to-date aerial imagery and their extracted road network features are thought as reference data, and the early GIS data are co-registered onto aerial images after orthorectified in order to interpret the remotely sensed image; (2) the vehicles are detected based on the co-registered data; (3) Based on the co-registered data, and recognized vehicle, the measurement including vehicle density, headway are conducted, further to compute the flow rate; (4) the experimental results are compared with the results from the ground-based detection. Finally, some conclusions are drawn up.

The US Department of Transportation (US DOT) initiated the Commercial Remote Sensing and Spatial Information Technology Application to Transportation program in 1999 in collaboration with the National Aeronautics and Space Administration (NASA). With the efforts in the past several years, tremendous accomplishments have been made. This paper makes a survey for the applications of land observation satellite remote sensing in transportation infrastructure and system engineering. The survey will be divided into the following fields: (1) remote sensing applied in transportation infrastructure, such as pavement construction and maintenance, and management; (2) Remote Sensing applied in transportation planning; (3) remote sensing applied in transportation safety analysis and monitoring; (4) remote sensing applied in transportation operation and analysis; and (5) remote sensing applied in transportation environmental analysis, such as ecosystem analysis, air pollution, etc. With an analysis of approximate over 150 academic papers and technical reports, some problems for remote sensing applied in transportation infrastructure and system engineering has been explored, and future development and application promising are concluded.

This paper presents a method for creating orthoimage in the urban area of extremely high buildings. The proposed method in this paper is different from the traditional methods, which improved the accuracy by increasing the number and/or improved the geometric distribution of ground control points. This proposed method first established a mathematical model of constraint condition on the building edges, such as perpendicularity, and then the established constraint conditions are merged into the orthorectification model. A test field located in downtown of Denver, CO, has been used to evaluate our methods. The experiments of comparing the accuracy achieved by our method and other methods are conducted. The experimental results demonstrated that the proposed method can improve the accuracy of 2-5 ft for those buildings of over 100 m high and even 5-7 ft for those buildings over 100 m high in the margin of imagery.

In this paper, a relational matching approach for imagery-to-GIS data is presented. This method applies image aspect interpretation and geospatial data mining techniques to realize their integration. Three-dimensional (3D) primitives, standing for house, are chosen, and their projections are represented by the aspects. The hierarchy aspect graphs are constructed to represent their connected relations. In this connection, the arcs are described by attribute data via the formulated coding regulations. The nodes of the graph represent image features and their attributes can contain measurements on these features. The arcs of the graph represent relations between features and their attributes can contain measurements on spatial relations. The data mining is used to discover the semantic relationship of these primitives. The aerial image is interpreted via these aspects and geospatial data mining. The experimental results demonstrated that the presented method is capable of effectively interpreting the aerial images and extracting the high accuracy of DBM (digital building model) at a rate of 83%.

This paper presents an integration method for co-registration between up-to-date aerial imagery and geospatial database with emphasis on road network data, which is called image-to-GIS co-registration. The up-to-date aerial imagery and their extracted features are thought as reference data, and the early GIS data are co-registered onto aerial images after orthorectified in order to integrated two datasets. The work includes two main steps: (1) The aerial image is first orthorectified into orthoimage; and (2) Road network information is extracted using GIS-assistant imagery mining techniques.

Orbital and suborbital-based remote sensing systems have been a stalwart for Earth observation sciences. In recent years, there is an increasing interest for civilian applications of low-cost aerial unmanned aerial vehicle (UAV). This paper presented design and implementation of a low-cost small civilian UAV system, including its field flight validation, system calibration, and mapping accuracy evaluation. This UAV system is specifically designed as an economical, moderately functional, small airborne platform intended to meet the requirement for fast-response to time-critical events in many small private sectors or government agencies for the small areas of interest. The UAV field flight test demonstrated that the designed low-cost UAV is capable of collecting clear and high-resolution video. A software system, called VideoOrthoring, is presented for processing the UAV-based video flow. The experimental results demonstrates that the planimetric accuracy of orthoimage can achieve 1∼2 pixels. The UAV-based orthoimage in combination with GIS-supported data from fast response to time-critical event, e.g., forest fire, is described.

There has been little investigation for the automatic extraction of power lines from aerial images due to the low resolution of aerial images in the past decades. With increasing aerial photogrammetric technology and sensor technology, it is possible for photogrammetrists to monitor the status of power lines. This letter analyzes the property of imaged power lines and presents an algorithm to automatically extract the power line from aerial images acquired by an aerial digital camera onboard a helicopter. This algorithm first uses a Radon transform to extract line segments of the power line, then uses the grouping method to link each segment, and finally applies the Kalman filter technology to connect the segments into an entire line. We compared our algorithm with the line mask detector method and the ratio line detector, and evaluated their performances. The experimental results demonstrated that our algorithm can successfully extract the power lines from aerial images regardless of background complexity. This presented method has successfully been applied in China National 863 project for power line surveillance, 3-D reconstruction, and modeling.

Video image matching to find tie points is one of important steps for merging dynamic, narrow field-of-view aerial video into a mosaic orthoimage. Because of its high data sampling rates and inherent characteristics of inconsistent and unstable flying of low-cost unmanned aerial vehicle (UAV), automatically matching of video data is still a big challenge and ongoing effort. This paper presents a self-adaptive image matching technique to automatically and effectively seek the conjugate points. The basic steps involves: (1) automatically extracting frame from video stream at real-time according to any given location or time slot, in order to save the post-processing time; (2) self-adaptively adjusting parallax of two neighbor frames to predict conjugate points to reduce searching space; and (3) the accuracy of image matching result is self-estimated using the technique of two-view co-planar geometry constructed by conjugated points and baseline. Based on the estimated accuracy value, the iteration for increasing or decreasing sample time slice to obtain new image pair is determined. The experimental results demonstrated that this proposed method can achieve high automation and real-time processing purpose for response to time-critical disaster applications.

High-resolution planimetric mapping generated from unmanned aerial vehicle video greatly attract many civilian users. To make such a mapping of natural disaster, exterior of parameters (EOPs) of video frames have to be exactly determined. To this end, onefromcoarsetofinecalibration method is developed in this paper, which include: (1) coarsely calculating camera IOP by using vanish point (VP) geometry; (2) Initially calculating Boresight matrix by simply selecting one pair of stereo frame to estimate photogrametry orientation parameters and compared to ones from the on-board GPS/INS; (3) A chain of high-overlapped video frames and valid tie points are rapidly generated based on developed data flow processing technology; (4) any EOP of generated video frames is solved based on cubic spline interpolating from all boresight aligned GPS/INS derived orientation parameters; (5) Taking tie points generated in Step 3 as observation, all EOPs solved in Step 4 and the camera's IOPs solved in Step 1 as unknown parameters, camera radial distortion as addition parameters, and a few non-traditional "ground control points" measured from registered USGS DEM and reference image, self-calibration bundle adjustment is applied to self-calibrate UAV video system. Using the calibrated EOP, IOP and USGS DEM, 2D planimetric mapping (orthoimage) is generated for each video frame individually, which are finally mosaicked automatically. The experimental results demonstrates that the planimetric accuracy of orthoimage can achieve 1~2 pixels. Some recommendations in application of UAV system for disaster management, e.g., forest fire, are made.

This paper presents a method of rapid automatic 3D building modeling and true texture mapping in urban area. The building model is extracted using polyhedral primitive based CSG model. With the building model, this paper addresses the true texture extraction method based on building model from aerial images. In order to accurately extracting texture information of building, accurate orientation parameters are needed. This approach also utilizes the geometric feature of building to match the model in order to get the accurate position. With this algorithm, the right texture of building roof and walls will be obtained. Based on the experimental result in Downtown, Denver, Colorado, it demonstrated that this method can effectively and rapidly reconstruct 3D buildings model with true texture in urban area.

To provide some advanced technical bases (algorithms and procedures) and experience needed for national large-scale digital orthophoto generation and revision of the Standards for National Large-Scale City Digital Orthophoto in the National Digital Orthophoto Program (NDOP), this paper presents a comprehensive study on theories, algorithms, and methods of large-scale urban orthoimage generation. The procedures of orthorectification for digital terrain model (DTM)-based and digital building model (DBM)-based orthoimage generation and their mergence for true orthoimage generation are discussed in detail. A method of compensating for building occlusions using photogrammetric geometry is developed. The data structure needed to model urban buildings for accurately generating urban orthoimages is presented. Shadow detection and removal, the optimization of seamline for automatic mosaic, and the radiometric balance of neighbor images are discussed. Street visibility analysis, including the relationship between flight height, building height, street width, and relative location of the street to the imaging center, is analyzed for complete true orthoimage generation. The experimental results demonstrated that our method can effectively and correctly orthorectify the displacements caused by terrain and buildings in urban large-scale aerial images.

The aim of the study is to analyze the seasonal development of crop canopies from the perspective of spatial domain of remotely sensed imagery for monitoring agricultural fields. The study site is located in eastern Nebraska, USA. Multidate high resolution aerial imagery using Kodak DCS420IR (including red and NIR bands) was acquired across the growing season over corn and soybean crops in 2000. Image datasets were first coregistered and then normalized using pseudoinvariant features, and NDVI was then computed. Spatial structure analyses were carried out using semivariograms. Results indicate that sill+nugget variance for NDVI component bands decreases as the crop develops for both corn and soybeans, suggesting the crop canopies become increasingly even, the range for red band also decreases for both crops but not for NIR band, whose range first increases but then decreases. Differential spatio-temporal patterns were found between corn and soybean NDVI for both sill+nugget variance and range, likely due to the difference in their developmental stage and canopy architecture. These findings suggest that NDVI is sensitive to the development of crop canopies and thus can be applied effectively to capture changing spatial structure of reflectance during the crop development

The at-sensor brightness temperatures measured by Terra ASTER and Landsat ETM+ and the surface temperature of ASTER were compared to one another in the study. The study area is located along the east coast from southern Virginia to the north of North Carolina, USA. Data sources consist of two adjacent pairs ASTER and ETM+ scenes, both acquired on the same day. All data were first converted to the physical temperature in degrees C, together with other necessary preprocesses before comparison. Results indicate that minimum, maximum, mean, and standard deviation of the at-sensor temperatures of these two sensors are very similar. Strong linear correlations (R2>0.99) were found between surface temperature and mean at-sensor brightness temperature for ASTER. However, inter-sensor comparison of measured temperatures, though overall still showing high correlation with R2values between 0.85 and 0.95, presents regular vertical columns in each scatter plot, and the variation range reached by these columns could be up to 10degC or more, which indicates that for different locations the temperatures measured by ETM+ may be the same but the corresponding temperatures measured by ASTER may vary by up to 10degC or more. This may suggest that there is some poor performance in Landsat-7 band 6 and warrants caution in applications when Landsat ETM+ thermal imagery is involved

The intra-urban variation in surface temperature and its related natural and social variables region by region within a city was investigated in the study. The study area is Washington DC, USA. Data sources include one EOS Terra ASTER scene, census data and high spatial resolution (1m) color infrared DOQQ. The census tracts were used to partition the city into different regions. Variables extracted and considered in the study are: (1) urban surface temperature, (2) population density, (3) NDVI, and (4) land use/cover, particularly the percentage of urban surfaces. Mean values of each variable were calculated based on the census tracts, and their interrelationships were examined using a correlation matrix. Results indicate that urban surface temperature is mostly correlated with the percentage of urban surfaces (r = 0.857). Whereas, NDVI shows strong negative correlation with surface temperature (r= - 0.817), and its coefficient is further subject to the influence of available water bodies (i.e., water bodies have low NDVI but can lower surface temperature). Positive relation was found between population density and temperature with a reduced degree of correlation (r = 0.417), but still significant (p-value <= 0.0001 at 95% confidence level)

The early procedures and algorithms for national digital orthophoto generation in the National Digital Orthophoto Program (NDOP) were based on earlier USGS mapping operations, such as field control, aerotriangulation (derived in the early 1920s), the quarter-quadrangle-centered (3.75 minutes of longitude and latitude in geographic extent), 1:40,000 aerial photographs, and 2.5 D digital elevation models. However, large-scale city orthophotos using early procedures have disclosed many shortcomings, e.g., ghost image, occlusion, shadow. Thus, to provide the technical base (algorithms, procedure) and experience needed for city large-scale digital orthophoto creation is essential for the near future national large-scale digital orthophoto deployment and the revision of the Standards for National Large-scale City Digital Orthophoto in National Digital Orthophoto Program (NDOP). This paper will report our initial research results as follows: (1) high-precision 3D city DSM generation through LIDAR data processing, (2) spatial objects/features extraction through surface material information and high-accuracy 3D DSM data, (3) 3D city model development, (4) algorithm development for generation of DTM-based orthophoto, and DBM-based orthophoto, (5) true orthophoto generation by merging DBM-based orthophoto and DTM-based orthophoto, and (6) automatic mosaic by optimizing and combining imagery from many perspectives.

This article presents a rigorous, high-precision model for geometric orthorectification of declassified intelligence satellite photography (DISP) imagery for the generation of a seamless, full-coverage mosaic of the Greenland ice sheet. This model integrates the bundle adjustment method and satellite orbital parameters, solving for interior orientation (including lens distortion) and exterior orientation parameters simultaneously. In addition, the techniques of adaptive filtering, bright-strip removal, radiometric balancing, and mosaic postprocessing are discussed. Two full-coverage mosaics of Greenland using 24 DISP images from eight orbits of the ARGON 9034A Mission of May 1962 and 36 images from 14 orbits of the 9058A/59A mission of October 1963 were created. The average planimetric accuracy (relative to the synthetic aperture radar (SAR) mosaic) is about 168 m from statistical measurements of 182 points in topographically flat areas and 186 m from statistical measurements of 201 points in mountainous areas. The two mosaic products have been delivered to the U.S. National Snow and Ice Data Center (NSIDC) for use by the research community.