Contextual road features and multiscale spatial semantic information play a vital role in road extraction from remote sensing (RS) images. However, accurately modeling these essential features with current convolutional neural network (CNN)-based road extraction algorithms remains challenging, leading to fragmented roads in occluded areas. Inspired by the self-attention mechanism of transformers in natural language processing (NLP), we propose an innovative MSMDFFNet in conjunction with Swin Transformer (SwinMSMDFFNet) for road extraction from RS images. First, the Swin Transformer is embedded as an auxiliary encoder into the MSMDFFNet to incorporate necessary self-attention mechanisms. Meanwhile, a multigranularity sampling (MGS) module is introduced to enhance the computation of self-attention at multiple granularities by the Swin Transformer. This module specifically transforms the feature maps produced by the main encoder into suitable inputs for the auxiliary encoder. Furthermore, to enhance the connections between adjacent local windows in the auxiliary encoder, a cross-directional fusion (CDF) module is designed for feeding the features of the auxiliary encoder back into the main encoder. Extensive experiments conducted on the DeepGlobe and LSRV datasets demonstrate that our proposed SwinMSMDFFNet has significant advantages in extracting road structure, particularly in areas with long-distance occlusions. It surpasses existing methods in pixel-level metrics such as F1 score, intersection over union (IoU), and connectivity metric average path length similarity (APLS). The code will be made publicly available at:https://github.com/wycloveinfall/SwinMSMDFFNet.

The challenge of computing electromagnetic (EM) scattering from rough surfaces is a notably complex yet crucial issue in the field of microwave remote sensing. The decomposition-reconstruction theory (DRT) has been previously introduced with the objective of reducing the computational complexity of scattering from rough surfaces. The central idea of DRT is to replace the scattering from rough surfaces with that from sinusoidal or other simpler, basic surfaces. The EM scattering from these simpler surfaces can be calculated more easily. The total scattering from the original rough surface is then reconstructed with the scattering from all the basic surfaces. This article introduces the decomposition reconstruction theory-based extended boundary condition method (DRT-EBCM), a novel method that combines the DRT and the extended boundary condition method (EBCM) to handle scattering from rough surfaces. The DRT-EBCM method decomposes the system integrals within the Dirichlet/Neumann matrices (system matrices) of the rough surface and represents them with the basic system integrals from complex sinusoidal surfaces (basic surfaces). This process leads to the derivation of new DRT-EBCM formulations. These new formulations not only establish a quantitative relationship between EM scattering from rough surfaces and basic surfaces, but they also significantly enhance computational efficiency due to the reduced complexity of the basic system integrals. Moreover, a new Fourier-Bessel (FB) function is introduced, which plays a crucial role in the scattering from basic surfaces. This function possesses a series of excellent properties that simplify the DRT-EBCM formulations and can potentially be applied in other scattering calculation methods. Simulation experiments were conducted to validate the proposed DRT-EBCM, confirming its correctness and validity. Furthermore, the application of DRT-EBCM in fast scattering calculation for rough surfaces was demonstrated. The results showed that DRT-EBCM significantly improves computational efficiency compared to the classical EBCM.

A stable and accurate Energy-Constrained T-matrix (EC-T-matrix) method is developed to calculate electromagnetic (EM) scattering from arbitrary rough surfaces. This method is proposed to enhance the stability and convergence of existing T-matrix methods. The conventional matrix inversion process is replaced with nonlinear programming to eliminate divergence caused by ill-conditioned matrix operations. The ill-conditioned matrix is systematically decomposed into a numerically stable block and an instable block. These blocks are then assigned as the objective function and linear constraint, respectively, in the optimization process. Furthermore, the law of energy conservation (unitarity condition) is implemented as a nonlinear constraint to ensure physically consistent scattering solutions. Numerical simulations using the proposed method are compared with established approaches including Stable Extended Boundary Condition Method (SEBCM), Small Perturbation Method (SPM), Small Slope Approximation (SSA), Truncated Extended Boundary Condition Method (TEBCM) and Multi-Level Fast Multiple Method (MLFMM). The stability and convergence of the EC-T-matrix method are confirmed by experimental results across diverse rough surface profiles. Stable convergent solutions are consistently achieved even under extreme corrugations. These results demonstrate superior accuracy and computational robustness compared to existing analytical and numerical methods.

Recently a novel active contour model referred to as fuzzy-based active contour model embedded with edge detector shows its good performance in the segmentation of images, however, it cannot be applied to Synthetic Aperture Radar (SAR) images directly due to speckle noise. In this model, the edge detector is not suited to detect boundary and the used intensity average information cannot distinguish different regions in SAR images. To this end, this paper proposes a modified fuzzy active contour model that incorporates edge and texture information for SAR image segmentation. First, number of false alarm defined in the improved line segment detector is modified as edge detection operator. Second, texture information obtained based on the textural image attained by Gabor filter is used instead of intensity average information. Third, edge and texture information obtained by the abovementioned operators is embedded in the energy functional related to the fuzzy active contour model and the segmentation is then achieved by minimizing this functional using a numerical iteration process. In the experiment, the segmentation results qualitative and quantitative validate the effectiveness of the proposed model.

Deep learning (DP) based road extraction methods often produce fragmented results. Direct optimization of end-to-end DP-based methods requires the design of more complex network structures to enhance the model’s adaptability in complex scenarios. To tackle this challenge, this paper adopts a novel approach that treats the discrepancy (the road breakage part) between road prediction and ground-truth as the extraction target, thereby constructing a highly efficient and lightweight semantic segmentation network, termed the Road Patch Extraction Network (RPE-Net). RPE-Net includes multi-directional striped residual (MDSR) encoder, multi-directional striped pooling (MDSP) units, and multi-directional striped decoder (MDSD). The structure is similar to LinkNet34, but the overall size of the network parameters is just 1.56MB, which is 1/50 of LinkNet34. The post-processing datasets used for training can be semi-automatically generated by the algorithm, and only a small amount of manual intervention can be used for training. A large number of experiments showt hat the post-processing method proposed in this paper has extremely high speed and generalization ability.

The completeness of road extraction is very important for road application. However, existing deep learning (DP) methods of extraction often generate fragmented results. The prime reason is that DP-based road extraction methods use square kernel convolution, which is challenging to learn long range contextual relationships of roads. The road often produce fractures in the local interference area. Besides, the quality of extraction results will be subjected to the resolution of remote sensing (RS) image. Generally, an algorithm will produce worse fragmentation when the used data differs from the resolution of the training set. To address these issues, we propose a novel road extraction framework for RS images, named the Multi-Scale and Multi-Direction Feature Fusion Network (MSMDFF-Net). This framework comprises three main components: the Multi-Directional Feature Fusion (MDFF) Initial Block, the Multi-Scale Residual (MSR) encoder, and the Multi-Directional Combined Fusion (MDCF) decoder. Firstly, according to the road’s morphological characteristics, we develop a strip convolution module with a direction parameter (SCM-D). Then, to make the extracted result more complete, four SCM-D with different directions are used to MDFF-Initial Block and MDCF-decoder. Finally, we incorporate an additional branch into the ResNet encoding module to build MSR-encoder for improving the generalization of the model on different resolution RS image. Extensive experiments on three popular datasets with different resolution (Massachusetts, DeepGlobe, and SpaceNet datasets) show that the proposed MSMDFF-Net achieves new state-of-the-art results. The code will be available at https://github.com/wycloveinfall/MSMDFF-NET.

This paper investigates the problem of building extraction from very high resolution (VHR) satellite imagery. Deep learning methods are deemed as emerging trends for solving this problem due to their increasingly prominent extraction effects. However, most state-of-the-art deep learning-based building segmentation methods produce pixel-level segmentation masks rather than accurate polygon-level extraction results required in real-world applications. This paper introduces a Harris function based active contour network (HACNet) that extracts polygon-level masks directly from satellite imagery. Our proposed HACNet not only leverages the full potential of active contour methods, but also successfully integrates the Harris function into the polygon contour evolution of the active contour models. The incorporation of the Harris function concentrates the attention of the neural network more on the vicinity of corners, thereby significantly improving the performance of building contour extraction, particularly in scenes with high curvature and noisy boundaries. Experiments on two public datasets (namely Vaihingen, Bing Huts) demonstrate the effectiveness and superiority of our model over other outstanding deep learning methods for the building extraction.

The channelized technology can be used in an IF (Intermediate Frequency) module which is a crucial component in a hyperspectral microwave radiometer. In this article, an effective method with which we can eliminate the effect of the transition band of a lowpass filter by designing a WOLA (Weighted Overlap-add) filter bank is put forward. The main idea of this method is to avoid spectrum aliasing at or near the boundary frequencies by constraining the parameters of a WOLA filter bank. To verify this method, we design a practical WOLA filter bank to measure the PSDs (Power Spectral Density) of a multitone signal and a broadband signal compared with a DFT (Discrete Fourier Transform) filter bank. The test results show the specific WOLA filter bank can measure the PSD of an input signal correctly even though the frequency components of input are located in or near the boundary frequencies. The specific WOLA filter bank can be used in an IF module in a hyperspectral microwave radiometer.

Correction of polarimetric synthetic aperture radar (PoSAR) data obtained from steep terrains is critical in SAR applications. Polarization rotation angle (POA) decides the data correction accuracy while the accurate estimation of it is challenging over forested regions. Aiming at this problem, this paper proposes a SAR data correction method for the region of mountainous forests based on a six-component scattering decomposition technique. First, POA is roughly estimated using orthogonal circular polarization method. Second, the six-component scattering decomposition technique is carried out to obtain helix scattering, oriented dipole scattering, and compound dipole scattering. Third, POA is precisely estimated according to the values of obtained scattering powers and co-herency matrix elements. Finally, SAR data is corrected using the estimated POA. The experiment carried out on a fully Pol-SAR image obtained from the region of mountainous forest verifies the effectiveness of the proposed method.

In this paper, the decomposition and reconstruction principle - two scale model (DRP-TSM) is firstly proposed to analyze microwave scattering of burned ground surface. According to the different states of ground surface before and after forest fires, we use the microwave scattering model to study the calculation of electromagnetic process of burned ground surface. Meanwhile, multi-band scattering coefficients of different polarization are measured to exploiting the temporal and spatial responses of forest fires effect on ground surface. Simultaneously, the small perturbation model is constructed in the DRP-TSM, which described as the combination of large-scale roughness and small-scale roughness for the accurate simulation of burned ground surface characteristics. Finally, the field experiments based on ground-based radar scatterometer (GBRS) are carried out and the observation is used for validation.

Using deep learning to extract roads from satellite images is one of the most popular methods. However, the existing encoder-decoder-based deep networks usually produce fragmented roads, due to the complex spatial and color characteristics of the road. In this paper, motivated by the road multi-scale information, we proposed a multi-scale and multi-direction feature fusion network (MSMDFF-Net) to reduce the fragmentation of road extraction results. The proposed method mainly consists of three processes: 1) In the initial stage, the image details from different directions were transmitted; 2) At different encoding stages, the multi-scale information of the image was fused; 3) In the decoding process, the matching modules of road characteristics were used to up-sample the feature map. Extensive experiments on the popular datasets (LSVD and Deep-Globe datasets) demonstrate that the MSMDFF-Net has higher accuracy and generalization performance with less fragmentary road results.

In order to make the best use of the available data in the remote sensing database, this paper focuses on an important topic of using the complementary information of multi-source remote sensing data, that is, road network extraction based on fusion technology with synthetic aperture radar (SAR) and optical images. Starting with the line segments achieved from the road segmentation maps, a decision-level fusion method which mainly includes two stages is proposed in this paper. The first stage is fusing based on the geometric overlapping rules. In the second stage, a road network extraction approach that takes into account both the contextual information and evidence theory is presented. The experiments on TerraSAR-X and WorldView-4 images showed that our proposed method had an excellent performance in terms of the completeness and quality of the road extraction.

For hyperspectral microwave radiometer, its intermediate frequency (IF) module need to divide the signal into thousands of channels. The ADC output of the IF module will up to dozens of GSPS. In order to process such huge data in real time, the FPGA of IF module needs to spend a lot of resources and energy to process the data in parallel, which limited the development of hyperspectral microwave radiometer. In this paper, a method to reduce the FPGA resource consumption for IF module of hyperspectral microwave radiometer is proposed. Using the relationship between odd and even sequences of real signals and complex FFT core, the utilization rate of FPGA resources is improved and the resource consumption of IF module is reduced effectively. The theories, resource consumption analyze, and test results are shown in this paper, which verified that this method can be applied to the optimize the IF Module of hyperspectral microwave radiometer very well.

In this article, the system design of the IF (Intermediate Frequency) module which is a crucial component of hyperspectral microwave radiometers is introduced. The IF module consists of 6.4GSPS sampling-rate ADC (Analog-To-Digital Convert) and Virtex-7 FPGA (Field Programmable Gate Array). The module is a real-time system with a frequency bandwidth of 3.2GHz and a spectral resolution of 6.25MHz. The 1024-point FFT (Fast Fourier Transform) algorithm which takes 160ns to accomplish an entire calculation is implemented in an FPGA. The effective number of channels of this module is 512 because of the symmetry conjugate of the FFT output. The test results of this module are given and analyzed, including the system response of deterministic signals and stochastic signals. The linearity of this module is also measured with the correlation coefficient which is up to 0.9999. The IF module can be widely used in hyperspectral microwave radiometers in various applications.

In this paper, an IF (intermediate frequency) module for hyperspectral microwave radiometers is introduced. The IF module is designed with two 6.4 GSPS sampling-rate ADCs and a Virtex-7 FPGA. The module can achieve 512 channels output for each ADC and process all the sampled data in realtime. The test results of this module are given and analyzed, such as linearity, sensitivity.

The performance of most supervised classification methods for synthetic aperture radar (SAR) images is largely tired to the number of samples, while labeled samples are usually very difficult and costly to obtain in the remotely sensing field. Semi-supervised methods, which are achieved by using pseudo samples, have thus been proposed to deal with this problem. Most of these methods, however, cannot be directly applied to fully polarization SAR (PolSAR) images due to the complexity of PolSAR data and it is also inefficient. To this end, this paper proposes a fast algorithm for the generation of pseudo samples of fully PolSAR data used in the classification, which is developed based on the complex Wishart distribution and chaotic maps. First, a weighting parameter that estimates the importance degree of labeled samples is defined in terms of the complex Wishart distribution. Second, chaotic maps are introduced to randomly and non-repeatedly select proper samples from the labeled sample set. Third, based on the selected samples with the weighting parameter, pseudo samples are generated. Finally, combined with appropriate classifiers, classification is attained. The experiment carried out on a fully PolSAR image verifies the effectiveness of the proposed algorithm.

A decomposition and reconstruction theorem (DRT) is introduced to advance computation and provide physical understanding for the scattering from fractal surfaces (FPs). The profile of FP is decomposed into test profiles (TPs), and the scattering of FP is reconstructed using the scattering of TPs to enhance the computational efficiency. A new method that applies DRT on the extended boundary condition method combined with the truncated singular value decomposition technique (TEBCM) is presented and referred to as TEBCM-DRT. The method of TEBCM-DRT is employed to solve the scattering from realistic soil surfaces, and the results are validated against other scattering models. Moreover, the efficiency of TEBCM-DRT and its validity range are investigated. The result shows that TEBCM-DRT improves computational efficiency to$1.95\times 10^{5}$and$7.35\times 10^{2}$times, respectively, compared to TEBCM and the conventional method of moments for a wide range of roughness. In addition, TEBCM-DRT indicates that the amplitude and direction of propagation of scattering modes are dependent on deterministic TPs. This relationship benefits for obtaining the accurate height of an arbitrary point on the profile from bistatic scattering coefficients.

This paper proposes a closed-form asymptotic solution for the bistatic coherent scattering of a multilayered rough surface structure based upon its effective dielectric constant (EDC) in specular direction. The EDC is modeled by establishing an equivalence of the coherent scattering between the multilayered rough surface structure and a half-space homogeneous medium. The scattering solution is then solved using the scalar Kirchhoff approximation (SKA) method. This new method is referred to as the SKA-EDC method, and it is applied to analyze the sensitivity of the EDC and coherent reflectivity to bare soils with realistic parameters at P- and L- bands. The result indicates that EDC can give different responses to the soil moisture variations with respect to the coherent reflectivity, enabling the potentials of root-zone soil moisture retrieval. At incidence angle smaller than 35°, EDC gives the same value for both polarizations, and the coherent reflectivity can show a significant response to soil at depths < 15-50 cm at 0.80 GHz and <5-15 cm at 1.57 GHz, depending on soil moisture. The simulation also demonstrates that subsurface roughness has a trivial effect on the EDC and coherent reflectivity for three-rough-surfaces separated by continuously dielectric profiles. Subsurface can thus be assumed to be flat for reducing uncertainty in soil moisture inversion algorithms when the subsurface roughness is unknown.

In this work, an IF(intermediate frequency) module of a hyperspectral microwave radiometer based on a polyphase filter bank (PFB) and Discrete Fourier Transformation (DFT)is introduced. The IF module is designed with an 800MSPS sampling-rate ADC and a Xilinx Virtex-7 FPGA. The module can achieve 512 channels and a bandwidth of 400M and process all the sampled data in real-time. The test results of this module are given and analyzed, such as linearity, accuracy, etc. It can be used in various applications of microwave remote sensing. The system has strong expandability.

Radiometric correction is a necessary pre-processing step in synthetic aperture radar (SAR) applications. In this step, geometric distorted areas caused by side-looking imaging system of SAR are the main reason for incorrect SAR data. This case is worse for rugged terrain since three different distorted phenomena including shortening, overlay, and shadowing can be observed. Generally, these three phenomena are roughly identified based on echo time of signal. However, many applications show that this method is not accurate that may affect subsequent data processing. To this end, this paper proposes a fast identification algorithm for the abovementioned three geometric distorted areas, which is attained based on the geometry model by using digital elevation model (DEM) data. In the proposed method, shadowing area is firstly identified by the defined look-up table in terms of geometry. Active overlay areas are then identified based on local incident angles. Passive overlay areas are finally identified using the traversing method. The experiment shows the efficiency and the effectiveness of the proposed method.

CNN (Convolutional Neural Networks) are inspired by the structure of the visual system and are one of the representative algorithms of deep learning. In recent years, Unet has become an acquaintance of Kaggle Challenge for its simplicity, efficiency and ability to extract features from small_scale samples. Since the model is designed for two classifications, there are serious problems of overfitting in using it for multiple classifications. Specifically, the model fits well in the train set, but there are many missed judgments, false judgments, and speckle noise in the test set. And the categories detected in the result graph are not balanced, some categories have better detection results, and some categories are hardly detected. To solve these problems, this paper proposes a new network that improves Unet: (1) Introduce dropout in the feature extraction stage to prevent overfitting; (2) Introduce ACNet to enhanced feature extraction capabilities. The performance of the new network which was trained with our own training dataset after data augmentation was evaluated with FWIoU and accuracy. Experimental results show that the network has higher FIWoU and accuracy rate under the same data.

As requirements for the spatial and temporal resolution of remote sensing images for Earth observations increase, the image data has exploded. However, a large amount of remote sensing image data requires a huge channel capacity for transmission. In order to improve the transmission efficiency, the images need to be compressed before transmission. Traditional fractal coding compression technology can greatly reduce the size of image files, but its blocking effect will blur the important texture information of remote sensing images. In addition, the extremely long fratal compression time makes it unsuitable for image transmission. To solve these problems, we propose a fast fractal coding method based on wavelet transform. This method can not only greatly increase the fractal coding speed, but also make the compressed image clear enough to retain texture information.

In this study, the back propagation neural network (BPNN) is applied to retrieve atmospheric temperature profiles, using 50~70 GHz passive microwave brightness temperature values. This paper makes a comparison of the retrieval of atmospheric temperature using the statistical inversion method and the BPNN inversion method. The results of the retrieval experiment show that the accuracy of the BPNN is better than that of the statistical inversion method in the upper atmosphere. It is of great application value to study the retrieve of atmospheric temperature profiles.

In order to speed up the update of existing road maps, it is crucial to develop a more efficient road extraction method from remote sensing images. In recent years, deep learning techniques have been widely used for road extraction applications. Among current CNN-based deep networks for road extraction, few works study the shape of the convolution kernel, and the remote contextual features dependency relationship is not fully utilized. In view of these problems, an improved DLinkNet is proposed in this paper. Firstly, a convolutional layer which fuses information of multiple scales is used to replace the InitBlock in the front of the network. Secondly, instead of using the D-Block, the DenseRe-Block is applied to the center structure of DLinkNet. The experimental results show that the improved network has a higher IoU score than DLinkNet when extracting roads from optical images in both city and mountain town areas.

Leaf area index (LAI) is very important for crop growth monitoring, biomass estimation, and many plant growth simulation models. Direct methods are the most accurate, but they have the disadvantage of being extremely time-consuming and labor-intensive as a consequence making them not be the preferred solutions. Indirect methods mainly include the radiometric method, inclined point quadrats, and hemispherical canopy photography. This paper focuses on the analysis of the principles and characteristics of the three indirect methods and affirms the advantages and development value of hemispheric canopy photography. The probability model is used to verify the feasibility of LAI inversion which is based on hemispheric canopy photography. In this paper, the canopy images of four sample plots on the campus were collected for LAI estimation, and the results were compared with LAI-2200C. Finally, the improvement of hemispherical canopy photography has prospected.

Fractal geometry function provides a practical way to simulate natural rough surface for electromagnetic scattering calculation of microwave remote sensing. In our study, we generate three-dimensional random rough surface based on band-limited Weierstrass-Mandelbrot model and carry out a series of quantitative analyses of the relation between fractal parameters and rough surface parameters. In order to eliminate the artifical reflection from the truncated boundary of rough surface model, a tapered incident wave model is built to illuminate the surface profile model with the use of sampling strategy. The tapered incident wave is introduced into the classical Two-Scale Model to calculate backscattering coefficient. The proposed model is applied to simulate the scattering of sea clutter based on the fractal sea surface model. Its simulation results are compared with real sea surface scatterometer measurement experiments in Beihai, Guangxi province and the overall deviation is less than 10%. The results validate the efficiency of the proposed model in obtaining the scattering simulation data on sea surface for parameter inversion of active microwave remote sensing.

With the development of deep learning technology, high-resolution remote sensing image change detection based on deep learning has become a hot topic in the field of remote sensing. However, the existing change detection methods based on deep learning only detect the change area of a specific object, and there is no public multi-objects change detection dataset. Focus on these problems, this paper proposed an end-to-end method to obtain the change detection results with change types for high resolution remote sensing images, including sample generation and a deep-learning network, called Res-Unet. Firstly, we obtain the label data by manual annotating. Then, co-registered image pairs are concatenated as an input for the network, and the multi-objects change detection results are directly generated by the network. The experimental results show that the method is effective and Res-Unet has a higher FWloU scores than U-Net.

Humidity is a critical parameter for microwave remote sensing. The method of the microwave resonator to measure humidity is fast, accurate, and real-time. In this paper, a microwave humidity sensor based on the substrate integrated waveguide resonator is designed. The sensor uses a hollow FR-4 substrate structure, which reduces the loss of FR-4 substrate at high frequency and improves the sensitivity of the sensor. The design has a simple structure, low cost, and simplifies the process of the humidity sensor based on thermodynamics and optics. Simulation and experiment results show that the sensor can measure the humidity well.

Slope stability models developed based on the physical mechanism of landslides show the effectiveness in landslide probability assessment, while they have rarely been applied in the field of radar remote sensing. Inspired by the related work, this article proposes a new quantitative method for rainfall-induced landslide probability assessment based on safety factors (SFs) using soil moisture estimation from synthetic aperture radar (SAR) images. In order to combine slope stability models with SAR measurement, first, soil moisture that plays a vital role in slope stability models is estimated by SAR techniques from vegetated slope terrain. In this article, we propose a new SAR data processing model for potential landslide areas and a modified physical-based scattering model for short vegetation. The estimated results are qualitatively verified by the tropical rainfall measuring mission (TRMM) instrument and are quantitatively verified by the field investigation. Second, we study the water table level that plays another vital role in slope stability models and cannot be retrieved from SAR data. The analysis indicates that it can be treated as a constant in the case of unsaturated soil moisture. Combining with other geotechnical parameters that do not change with external circumstances, we simplify the slope stability model, of which effectiveness is tested by the visual interpretation. Finally, the SF maps are obtained by the simplified slope stability model using soil moisture estimated from the corresponding SAR images. The field investigation shows that all the observed landslides are located in the unstable areas, indirectly verifying the proposed method.

DlinkNet[1] (LinkNet With Pretrained Encoder and Dilated Convolution) has been proved to be an effective method for road extraction in remote sensing fields as it won the champion in the DeepGlobe's Road Extraction Challenge. However, as the number of hyperparameters increases (such as the number of channels, filter size, etc.), the difficulty and computational overhead of network design will increase. Focused on this problem, this paper put forward effective ideas to improve D-LinkNet: (1) Applying ResNeXt as its backbone instead of ResNet to rebuild D-LinkNet; (2) Replacing initial block with stem block in the beginning of the network. The results of road extraction which was trained with our own dataset was evaluated with IoU scores. The evaluation results shows that the improved network has higher IoU scores than D-LinkNet when maintaining the model complexity and number of parameters.

Soil moisture is a critical parameter for microwave remote sensing. In this paper, a probe based on a microstrip resonance ring (MRR) is designed. The fitting curve between resonant frequency, Q-factor, and complex dielectric constant are obtained by electromagnetic simulation. To measure the moisture of soil, a density-independent factor is introduced to remove the effect of density of soil, which makes this sensor more accurate and easy to use, especially suitable for fieldwork. The experimental results show that this sensor works very well in measuring moisture and complex dielectric constant of soil.

In this work, an IF (intermediate frequency) module of a hyperspectral microwave radiometer is introduced. The IF module is designed with a 5GSPS sampling-rate ADC and a Virtex-7 FPGA. The module can achieve 512 channels and process all the sampled data in real-time. The test results of this module are given and analyzed, such as linearity, sensitivity, etc. It can be used in various applications of microwave remote sensing.

Atmospheric fine particulate matter (PM2.5) pollution has brought a strong focus on public health and environmental quality in China because of its adverse effects. To evaluate the effect of technological and social development on environmental quality in China, it's in urgent need of understanding the long-term spatiotemporal trend of PM2.5 concentrations. In this paper, satellite-derived annual mean PM2.5 estimates (1998-2016) were validated using ground-based PM2.5 measurements (2015-2016) and then used for spatiotemporal trend analysis. The results indicated that national mean PM2.5 concentrations in China increased primarily before 2008, and then decreased. The spatial distribution of PM2.5 concentration is high level in the east and while low in the west of Heihe-Tengchong Line in China. Our findings provided a profound understanding of PM2.5 variations and affirmed the effectiveness of implemented measures of reducing PM2.5 loadings in China, offering important reference data for relevant policy making of air pollution prevention in the future.

The hyperspectral microwave radiometer has a large number of channels, which results in the decrease of the measuring signal sensitivity. We propose two optimizations based on the design of the hyperspectral microwave radiometer, one is the increase of word length of the system; the other is the improvement of the window function. These optimizations not only effectively reduce the noise level of the system, but also have certain suppression effects on the signal spectrum leakage. Firstly, this article introduces the advantages of the hyperspectral microwave radiometer in the field of microwave remote sensing. Secondly, the structure of the IF module digital circuit of the microwave radiometer is established. Finally, some deficiencies in this system are obtained through theoretical derivation. The radiometer intermediate frequency module is optimized to improve the measurement sensitivity of the hyperspectral microwave radiometer.

In the previous work, the presented multi-texture-based segmentation model based on level sets for synthetic aperture radar (SAR) images generally can attain good results. However, the weighting parameters in this model have non-ignorable influences on segmentation performance, while such parameters usually are determined by the past experiences, which decreases the flexibility of this method. To address this problem, based on this model, we propose a SAR image segmentation method with the optimal level sets using chaotic whale optimization algorithm (CWOA). First, various image segmentation results are attained by the multi-texture-based model with several random sets of weighting parameters, samples are then automatically generated by comparing these segmentation results. Second, search agents (humpback whales) are defined with respect to the weighting parameters that need to be optimized, and the fitness function (prey) is associated with the samples. The optimal level sets are then established by integrating the multi-texture-based model and CWOA. Finally, the experimental result achieved from a SAR image shows the effectiveness of the proposed method.

Dual-polarization synthetic aperture radar (SAR) data acquired over steep terrain topography is commonly calibrated using the correction methods for single-polarization intensity SAR data. Such kinds of correction methods are generally attained by the illuminated area normalization based on terrain geometry while the case of polarization rotation induced by steep terrain is ignored. To address this issue, this paper proposes a correction method for dual-polarization SAR data over steep terrain, in which both effects of geometry and polarization rotation are taken into account. In this method, correction is achieved based on the original definition of backscattering coefficient by using only intensity SAR values obtained from two channels (co- and cross-polarization) without involving any other amplitude or phase information. The experiments on a set of dual-polarization data correction show that the corrected results generally agree with the facts of relation among SAR data distortion, terrain geometry, and polarization rotation with respect to azimuth and range slope angles of radar, which verifies the effectiveness of the proposed correction method.

Due to different imaging mechanisms, the registration of optical and Synthetic Aperture Radar (SAR) image is a very challenging task. Many optical and SAR registration methods have been proposed. But most of them are for low-to-medium resolution images, and less for high-resolution images. Therefore, this paper proposes a high-resolution optical and SAR image registration method using local self-similar descriptor based on edge feature. Firstly, a Gauss-Gamma bi-windows algorithm is used to extract the edge intensity maps of the images respectively. Its function is to eliminate the non-linear gray-scale difference between SAR and optical images, and also to avoid the interference of isolated speckle noise on feature point extraction. Then, local self-similar descriptor is extracted on the edge intensity map, and descriptor matching is performed using Euclidean distance. Finally, the fast sample consensus algorithm is used to eliminate mismatched point pairs. The experimental results can effectively resist speckle noise and radiation differences, and obtain pixel-level registration accuracy.

This paper proposes a semi-empirical microwave model of methane emissions (CH4) based on the biogeochemical processes from rice paddy. By exploiting the mechanism processes of methane production, oxidation and emission, a combined microwave model is developed to predict methane emissions from rice paddy. Simultaneously, the main influencing emissions factors, which concluded soil, underlying water body, vegetation, climate and management, are analyzed in the present model. During the whole growth season, the multi-polarization backscattering coefficients of rice are measured by the ground-based radar scatterometer (GBRS), and relevant parameters are observed in the rice fields. Moreover, experiments of the emissions samples are conducted on conventional static box, and the results are compared to the semi-empirical microwave model and Denitrification-Decomposition (DNDC) simulation model, respectively, which shows the good extend performance is developed from experience model into mechanism model based on active microwave remote sensing data.

Based on microwave backscattering characteristics of typical surface features on wildland-urban interface (WUI) fire, dynamic change rules of key factors of WUI fires are explored, and fire assessment methods are established. In terms of parameter retrieval, ground-based scatterometer and satellite-borne SAR image are utilized for synchronous measurement of multi-polarization data. Considering microwave-based empirical model and minor scattering component tracking algorithm, WUI vegetation biomass, WUI vegetation water content, WUI underlying soil moisture and WUI surface feature aggregation are extracted in accordance with microwave scattering mechanism. Meanwhile, in combination with meteorological and environmental parameters, key parameters for the monitoring of WUI fires are selected to construct a fire monitoring model incorporating WUI characteristics, thus offering a new idea and new tool for remote-sensing monitoring of WUI fires.

With the continuous development of optical lens and imaging chip technology, the fisheye camera method has been widely studied in the world because of its characteristics of TRAC instrument and LAI-2200C coronal analyzer. To obtain the critical ecological parameter of the leaf area index at low cost, a synchronous LAI-2200C leaf area index hemispheric image acquisition system was proposed in this paper. The system consists of an embedded platform, a low-cost image sensor, and a fisheye lens, fixed to the LAI-2200C optical sensor detector, triggered by the LAI-2200C synchronous of hemispheric vegetation image. This study used the system to measure the coronary photos of the tall shrubs in the Chengdu area at different times. It used an image processing algorithm to analyze and obtain LAI. The results show that there is a significant linear correlation between LAI and LAI-2200C measurements obtained by the Fisheye Camera Method (DH-P) (R2= 0.814), the average square root error is 0.278.The test results show that the system can effectively collect the image of the vegetation canopy can be low-cost, and obtain a leaf area index results with minor error.

Extracting road information from remote sensing images is one of the hot topics in image processing. The extraction result is saved as raster data, which is difficult to spatial information query, so it's necessary to convert it into vector data. Existing raster data vectorization methods are difficult to maintain the shape of roads and the connection between them, and don't take the attribute information into account. Focus on these problems, this paper proposed an algorithm for raster data vectorization. The algorithm obtains road by tracking pixels in the image, and the road is segmented by nodes (endpoints and intersections), which ensures that their connections in vector data are not disrupted. Then match them with corresponding attribute information (material and width) by image masking. Finally, vector data with attribute information is obtained. The experimental results show that the accuracy of the connection between roads is 97.5% in the vector data obtained by this method, and it has a correct rate of 95% for attribute matching.

The IF receiver is an essential part in hypspectral radiometer. In this paper, we present an improved structure of chirp transform spectrometer (CTS) for hyperspectral radiation measurement. The traditional two-channel structure of CTS based on radar pulse compression technique has many advantages as well as some limitations. Considering the limitations, the traditional two-channel symmetrical structure is modified to a simple structure for stationary signal measurement. The working principle of the improved structure is described in detail and its limitation of application is analysized. Two simulation models for the traditional structure and the improved one are built in Advanced Design System. Results demonstrate that the distribution of time domain pulses of the novel structure is the same with that of the traditional structure. The amplitude accuracy, dynamic range and frequency resolution are also the same. Compared to the traditional structure, the improved structure saves components, power consumption and has less weight and more simple control system, which is suitable for practical application especially in aviation and space.

Leaf area index (LAI) is the basic factor to understand canopy productivity, soil water evaporation, total transpiration loss, and soil temperature. On the basis of analyzing the merits and demerits of various LAI measurement methods, this paper affirms the development prospect and application value of the hemispherical image method. The paper focuses on the inversion theory of LAI and the extraction of canopy porosity. The essence of the hemispherical image method is further elaborated: after the canopy porosity is obtained by image processing, LAI is retrieved based on Lambert-Beer law. In this paper, four tall arbor forests of Chengdu city are selected as research objects to explore the method of obtaining LAI by hemispherical images and compare with LAI-2200C Plant Canopy Analyzer. The results show that LAI measurement based on the hemispherical image is feasible and credible.

This article proposes an unsupervised multiregion segmentation method for fully polarimetric synthetic aperture radar (polSAR) images based on the improved fuzzy active contour model. Different from most of the active contour models that are based on the utilization of only statistical information, the proposed method makes better use of information from polarimetric data. In addition to the statistical information, an edge detector modified from the ratio of exponentially weighted averages (ROEWA) operator, a sliding window algorithm for the total received power, and a ratio operator with respect to scattering mechanisms are integrated to the proposed active contour model. We then present a layer-based fuzzy active contour framework to solve our model. The general fuzzy active contour framework is computationally much more efficient compared with the level set-based framework; however, it cannot be applied to the multiregion segmentation of SAR images due to its low robustness to strong noise. The proposed approach includes the advantages of the general fuzzy active contour framework and has good robustness. Using two fully polSAR images demonstrates that the proposed method can achieve higher efficiency and a better segmentation performance in comparison with the commonly used active contour methods.

The objective of this article is to investigate a multiphase segmentation framework for synthetic aperture radar (SAR) images, which is proposed based on the idea of the fuzzy region competition-based method. The fuzzy region competition-based framework is highly efficient and can attain good segmentation performances for conventional images. The framework is achieved based on its convexity, which not only ensures the existence of a globally optimized solution but also enables the convex optimization theory-based solving algorithms that are feasible. However, the constraint conditions of the framework that guarantee this convexity probably cannot be satisfied in the segmentation of images corrupted with strong noise. Therefore, applying this method to an SAR image probably produces an unsatisfactory segmentation result. To address this problem, we propose an improved fuzzy region competition-based framework in terms of the hierarchical strategy, such that the framework is always convex during the iterative calculation. The proposed framework inherits the advantages of the fuzzy region competition-based method, as well as that it is able to be applied to the segmentation of images with strong noise. Several experiments are then carried out to test and verify the performance and the robustness of the proposed framework. It demonstrates that the proposed segmentation framework can be applied to various types of SAR images and achieves satisfactory segmentation results.

Recently, D-LinkNet has become a popular convolutional neural network for its high IoU scores in road extraction. Actually, D-LinkNet could have higher IoU scores if it uses ResNet that has deeper network as its encoder part, but the scale of the model would be very huge because of the center part design called DBlock in this paper. Focused on the problem, this paper made some improvements: (1) Add a 1 × 1 convolution that can be introduced as bottleneck layer before DBlock to reduce the number of input feature-maps, so as reduce the parameters and improve computational efficiency. Then add another 1 × 1 convolution after DBlock to recover the required output channels; (2) Rebuild the entire network based on ResNet units with new structure instead of the original one. The new network named as D-LinkNetPlus. Except for reducing parameters, this paper also proposed a method named ESIPs to eliminate small independent patches for the accuracy improvement of road extraction. The experimental results show that the D-LinkNetPlus not only has less parameters but also has a higher accuracy than the results gotten by original structure of D-LinkNet. In addition, the road extraction results presented a better visual effect after processing by ESIPs proposed in the paper.

The transmission line method for measuring the complex permittivity of material via the S-parameter has been widely researched. In this paper, the rationale of the transmission line method is derived clearly, especially for the optimized solutions for the multivalued issue and the thickness resonance are presented. The revised algorithm is employed to evaluated the complex permittivities of soil in terms of various of water content at frequencies between 500MHz and 10GHz. The experiment shows that there not exist the multivalued issue and the thickness resonance in the retrieving results, it is indicated that the complex permittivities of soil can be obtained by the advanced transmission line method.

CNN (Convolutional Neural Networks) has been proved to be an effective method for road extraction in remote sensing fields recently. D-LinkNet based on LinkNet adopted consecutive dilation convolution with different expanding rate to enlarge the receptive field without reducing the resolution of the feature-maps so that had an outstanding performance in high resolution satellite imagery road extraction. However, too many parameters make some inadequacies for D-LinkNet, which introduced LinkNet as its backbone with ResNet construction. Focused on this problem, this paper proposed a new network to improve D-LinkNet: (1) Rebuild D-LinkNet by applying DenseNet as its backbone instead of ResNet; (2) Replacing initial block with stem block in the beginning of the network. The accuracy of road extraction results from the new network which was trained with our own training dataset after data augmentation was evaluated with IoU scores. The experimental results show that the proposed network has a higher IoU scores than D-LinkNet with less parameters.

This paper investigates the backscattering properties of vegetation fire based on ground-based scatterometer measurement in the combustion period. According to the different states of combustion during and after vegetation fire, respectively, X-band and C-band scattering coefficient of full polarization (HH, HV, VH, and VV ) are measured to exploiting the spatial and temporal responses of fire effect on vegetation. Meanwhile, the relevant influencing factors, which include the parameters of vegetation, ground and environment, are obtained in observation area. Besides the process before and after fire, it is discussed in detail for analysis of microwave scattering coefficients changing with different burning degrees in the fire development process. Based on the experimental result, the distinction through combustion is shown by the microwave method effectively, which proves its potential in information extraction from vegetation fire.

Interferometric synthetic aperture radar (InSAR) has become the primary means to obtain digital elevation models(DEM) on the earth's surface, including several key steps such as removal of flatten effect, filter processing, and phase unwrapping. Due to atmospheric interference, etc., the distribution of azimuth and range to phase quality on SAR images is uneven. Thus, this paper proposes a method based on weighted quality graph to guide phase unwrapping, which considers the difference of the contribution weight of spatial noise to the range and azimuth of the quality graph. The weighting method is used to eliminate the error of the range direction and the azimuth direction, form a new quality graph, and guide the phase unwrapping. In order to solve the problem of slow speed of traditional methods, this paper introduces the method of heapsort to improve the speed of phase unwrapping.

This paper introduces a method of road extraction using dual-temporal high-resolution synthetic aperture radar (SAR) images. Firstly, multiplicative Duda operators are applied to detect line features. Then, coherence and backscattering coefficient are combined to distinguish road from river and shadow and the coefficient of variation is used to remove heterogeneous areas. Next, road is segmented via path opening and thresholding. At last, a novel thinning and gap connection approach is proposed to gain the thinned and more complete road map. The experiments were test on TerraSAR-X images and results showed that the proposed method improved considerably the results of road extraction compared with approach using single-temporal SAR image.

The extraction of road information has always played a quite important role in civil and military. With the gradual maturity of road extraction technology, how to automatically extract road pavement material information has also begun to attract the attention of researchers. Aiming at this problem, this paper proposes a road material analysis method based on image features and Support Vector Machine (SVM). The method extracts the road surface portion of the road based on the road binary image. Then we use the Rerinex algorithm [1] to denoise the image, the HSV (Hue, Saturation, Value) color model, Local Binary Patterns texture [2], and Gray Level Co-occurrence Matrix (GLCM) are used to extract the features of the road surface pixels. After the principal component analysis reducing the dimension, each feature vector is fused. Then we use the support vector machine (SVM) classifier to analyze the material (asphalt concrete road, cement concrete road and bare soil road) information of the road. This method that combines multiple image features is a new application extension of remote sensing image based on road extraction. The results of the classification experiments on remote sensing images confirmed that the method is effective.

A novel formulation for the calculation of equivalent dielectric constant of multilayer medium with slightly rough interfaces is proposed. The formulation is based on the volumetric perturbation of inhomogeneous medium and the closed-form solution to the problem of electromagnetic scattering from the simplified geometric structure of layered media. And the derivation relies on the reciprocity principle which can use the perturbation of dielectric constant to replace the height fluctuation of rough interface contours. According to the calculation results, the validity of macroscopic dielectric constant model is tested to a certain extent. Furthermore, the simulation of four-layer soils and ice-covered regions will be directly applicable for active microwave remote sensing inversion.

Cereal crops, contrary to natural vegetation, have the different characteristics for their regular planting. Further, the random assumption of the radiative transfer theory is not suitable for cereal canopy. The paper aimed to present a modified scattering model of row wheat at X-band (center frequency 3.2GHz). The modified scattering model considered both the surface scattering of soil and the volume scattering of wheat canopy. In different wheat growth stage, the weights of the two kinds of scattering phenomenon were set up based on an empirical growth model because of their visible area. A series of data including wheat growth parameters and backscatter coefficients, related to the interaction, were collected for the analyses of the model. The research results showed the model could better reflect the scattering phenomenon of regulate planting, which is helpful to agriculture remote sensing fields.

Resolution is one of the important indices to measure the quality of SAR images. Super-resolution reconstruction is a widely adopted resolution enhancement method. Many algorithms have been developed for the super-resolution reconstruction. Among these algorithms, this paper applies projections onto convex sets algorithm to SAR image reconstruction processing. The POCS can efficiently obtain high-resolution SAR images with enhanced details. However, the POCS requires many low-resolution SAR images of the same area to gain a better result, usually 10 to 20 images. Such requirement is very difficult to achieve when only single-polarization mode is included. In this paper, we propose a novel method that utilizes all the polarimetric images of the same original SAR data for the algorithm. Thus, the number of the available images is increased exponentially. The experiment results have demonstrated the effectiveness of our proposed method: The reconstructed high-resolution SAR image based on multi-polarimetric information is more detailed and clearer than that based on single-polarization information.

The paper uses the PSO-SVR algorithm to inverse the water quality parameters based on GF-1 remote sensing image in Longquan lake where is located in Chengdu, Sichuan Province. Longquan Lake is a key drinking water source in Chengdu, so its water quality is very critical. Particle swarm optimization (PSO) optimizes the parameters of the support vector regression (SVR) inversion model to establish the new PSO-SVR inversion model, and PSO can effectively improve the efficiency and the accuracy of the SVR inversion model. At the same, the empirical inversion model was established by using the measured hyperspectral data and concentration of water quality parameters. Comparing with SVR inversion model, PSO-SVR inversion model achieves a better result in the application of suspended solids and Chlorophyll concentration inversion.

In this paper, a novel method is proposed to evaluate the scattered field from rough surfaces by decomposition-reconstitution. The extended boundary condition method (EBCM) is employed in conjunction with a Weierstrass Mandelbrot (WM) function for fractal surface profile and its components. WM function can be decomposed into a series of sinusoidal profile. The properties of the sinusoidal profile and WM function to be periodic and almost periodic allow the deriving respective amplitudes of the scattered Floquet modes. The superposition of scattered fields from each sinusoidal component is consistent with the scattered field from WM surface. The result has compared with AIEM in backscattering coefficients. Thus, by calculating the scattered information for each sinusoidal component of the rough surface, we can obtain the overall scattered field.

The research on electromagnetic scattering from random rough surface plays an important role on the scientific investigation and engineering applications of the microwave remote sensing. In this paper, finite difference time domain (FDTD) is employed to calculate the backscattering coefficients from soil surface in terms of angular, frequency, RMS height, correlation length and moisture, the errors are less than 3dB between the numerical simulation and advanced integral equation model (AIEM) in most cases. This technique is also used to solve the backscattering coefficient of the bare soil surface of Qionglai, Sichuan, the angular trends are generally in good consistency with the ground-based scatterometer data.

In this paper, the decomposition-reconstitution theorem is introduced to solve the electromagnetic scattering field from the random rough surface. The random rough surface is decomposed into a series of fractal described by sinusoidal basis functions and the scattering fields are computed for each fractal. The scattering field of surface is reconstituted by vector-superposing the scattering fields of fractal. The theorem is demonstrated by the numerical simulation using FDTD. It shows that the difference of both results is a little in the range of the calculation error. This theorem was applied to solving the backscattering coefficient of the soil surface of Qionglai Sichuan, the result generally coincides with the ground-based scatter-meter measurement data and AIEM.

In this paper, a method of Numerical Maxwell Model in 3D Simulations (NMM3D) is used to evaluate the backscattering information of fractal rough surfaces. Weierstrass Mandelbrot function is employed to generate natural rough surfaces. Quantitative analyses are demonstrated on the relation of fractal parameters and rough surface parameters. In order to eliminate the unwanted edge effects, a tapered incident wave model is built to illuminate the surface models with the use of sampling strategy. Method of Moments is employed to evaluate the backscattered fields. The results are demonstrated and show a good agreement with AIEM model.

Semantic segmentation using adversarial networks has been approved to produce the better artificial results in image processing fields. Focused on current Deep Convolutional Neural Networks (DCNNs), since the convolutional kernel size has been fixed in every convolutional operation, the small objects would be ignored with large convolutional kernel size, and the segmentation result of large objects is not continuous with small convolutional kernel size. The paper developed a semantic segmentation model that combined the adversarial networks with multi-scale context aggregation. Further, the model was applied to road segmentation of UAV RS images. The experimental results of this semantic segmentation model with multi-scale context aggregation has a better performance for road segmentation and fit well with the reference standard results. It can improve the road segmentation accuracy obviously in the situation where there are other small regions whose shape or color is similar to road regions in UAV RS images.

In order to accelerate the calculation of the similarity metric between two patches in non-local means filter of polarimetric synthetic aperture radar (PolSAR) images, this paper proposes a weighted acceleration algorithm to eliminate redundant operations. It can greatly reduce the computational complexity by using the accumulated covariance data to avoid repeated computations. Furthermore, the algorithm introduces the polarization similarity parameter to preserve the details of the image. It also processes the covariance matrix of each pixel. Thus, the polarimetric properties are maintained. The results of the C-band RadarSat-2 data are provided to demonstrate its rapidity and competitive general performance of speckle reduction and edge preservation.

Tomography SAR (TomoSAR) imaging can distinguish different scatterers in the same pixel using their spatial frequency spectrum difference. Sparse scatterers case located in one pixel can be imaged using high resolution spectral estimation algorithm or compressed sensing (CS) algorithm. For the pixel where the top position of the electric power transmission tower is located, it can be seen this sparse scatterers case. In this paper, TomoSAR imaging methods based on MUSIC algorithm, ESPRIT algorithm and CS algorithm are used to calculate the height of the electric power transmission tower, respectively. TerraSAR-X data is used for experiment. The experimental results demonstrate the ESPRIT algorithm has better accuracy than the other two algorithms for the height estimation of the study electric power transmission tower. Basically, this accuracy can meet the requirement in practical applications.

The complex terrain, frequent movement of the earth's crust, dense vegetation exists in the western sichuan plateau where has frequent disasters. PSInSAR can extract the target with stable, strong scattering characteristics to monitor deformation effectively. The experimental area is in maoxian, erlang- mountain, chengdu. Paper uses, Pseudo-3D Phase Unwrapping and LAMBDA Method respectively related to GPS integer ambiguity resolution to unwrap phase and extract the surface deformation of the research area, and comprehensively analyze the effects of different surface and different solutions to the results. Experimental results show that the Network Adjustment Method in elevation correction and linear deformation rate has a comparative advantage, can be more applicable to maoxian, mountain complex mountainous area surrounding the transmission channel of deformation monitoring.

In the electric power transmission corridor areas, the ground surface deformation will influence the inclination of the electric power transmission towers even after the collapse of the towers. Due to the layover, traditional differential synthetic aperture radar interferometry (DINSAR) cannot be used to obtain the inclination displacement of the general line shape and vertical coherent objects such as electric power transmission towers fixed vertically in the ground. Based on the improved DINSAR (IM-DINSAR) model, this letter proposes a revised implementing algorithm for IM-DINSAR model. It can remove the top-bottom vertical-height interferometry phase of the tower (caused by the height of the tower itself, similar to the flat earth interferometry phase in traditional DINSAR) by only using the information of every tower itself. This is different from the implementing algorithm proposed. Then, we use the residual differential interferometry phase after phase unwrapping to obtain the inclination displacement of top position of the tower. Moreover, this letter analyzes atmospheric phase, noise, and multitemporal series cases in IM-DINSAR model. Simulation results demonstrate the effectiveness of the model proposed in this letter.

Synthetic Aperture Radar (SAR) image segmentation is a difficult problem due to the presence of strong multiplicative noise. To attain multi-region segmentation for SAR images, this paper presents a parametric segmentation method based on the multi-texture model with level sets. Segmentation is achieved by solving level set functions obtained from minimizing the proposed energy functional. To fully utilize image information, edge feature and region information are both included in the energy functional. For the need of level set evolution, the Ratio of Exponentially Weighted Averages (ROEWA) operator is modified to obtain edge feature. Region information is obtained by the Improved Edgeworth Series Expansion (IESE), which can adaptively model a SAR image distribution with respect to various kinds of regions. The performance of the proposed method is verified by three high resolution SAR images. The experimental results demonstrate that SAR images can be segmented into multiple regions accurately without any speckle pre-processing steps by the proposed method.

Vegetation coverage is an important indicator for forecasting geological disasters in mountainous areas such as landslide. However, it is a challenge to extract vegetation coverage in complex terrain from SAR image. A major problem is that the variation of the backscatter coefficient of the same object varies with the local incidence angle. As a result, a large number of discrete points appear in the classification results. Focus on this problem, a region-based method of vegetation coverage monitoring has been presented in this paper. Based on the results of yamaguchi decomposition and SVM algorithm, the Watershed algorithm is used to over-segment the image and regions are merged on a pixel-voting basis. When the modified method was applied on RADARSAT-2 data, the research results show that the completeness and correctness are improved compared with the method based on yamaguchi decomposition and svm algorithm.

This paper made a coherence analysis to access the ability of snow extraction based on the Terra-SAR X-HH data, and develop a method using multi-temporal coherence combined with the traditional method. Two pairs of Terra-SAR data with the same temporal baseline and similar spatial baseline were chosen, before and after snowfall. Traditional method was used to remove the snow free area with relative higher coherence. Differences between two coherence data was taken to distinguish snow covered from the snow free area which shows low coherence value under X-band condition. Backscatter coefficient data was used as well. The result was verified using GF-1 optical image. An accuracy of 82.44% was achieved if we consider the result from GF-1 optical image as `ground true'. Google Earth image was also used as verification which shows a good estimation of the snow covered areas.

High temporal and spactial resolution aerosol retrieval is a difficult task because of the absence of corresponding satellite data. Due to the lack of a shortwave infrared band near 2.1 um aboard on Gaofen-4 instrument, which is critical for determining surface reflectance. In this paper, a new algorithm resolving the problem based on Gaofen-4 that was placed in Geosynchronous (GEO) orbit and Landsat-8 data was proposed. Also, Gaofen-4 sensor band mean solar irradiance (BMSI) was calculated which was not open to public until now. In the algorithm, normalized difference vegetation index (NDVI) was used to identify dark target pixels that have certain linear relationship over blue and red bands' surface reflectance. In order to remove Gaofen-4's geometric deformation, the data were processed with RPC Landsat-8 panchromatic data. The algorithm was applied to two cities, Beijing and Chengdu. The result, aerosol optical thickness (AOT) with a 50m × 50m resolution, indicated the algorithm can be effective for vegetation area or low surface reflectance area. The algorithm is very useful and significant for environmental protection, air quality monitoring and atmospheric pollutants sources tracing.

This paper presents an unsupervised segmentation method based on the variational model for fully polarimetric Synthetic Aperture Radar (PolSAR) images. Considering that fully PolSAR images contain much more information than optical or single-channel SAR images, we used the characteristics vector of PolSAR images instead of statistical parametric models in the variational model. To fully utilize characteristics information, we propose a ratio operator with respect to scattering mechanisms and a sliding window algorithm for total received power. Combining these two operators with statistical information, the variational model with respect to an energy functional is defined and the segmentation is then achieved by solving such functional in terms of fuzzy membership functions and dual projection method. The experimental results indicate that the proposed method can attain a better segmentation compared with the classical cluster algorithm based on the complex Wishart distribution and the variation model only using statistical information.

In this paper, the dynamic programming algorithm (DPA) is firstly proposed to analyze microwave minor scattering from advanced Michigan microwave canopy scattering (MIMICS). According to the decision stages which divided by the incidence angle, we use the DPA to study the underlying submerge information under the vegetation. Simultaneously, constructed in the DPA, the multidimensional sequence of state and recursive strategy is derived from the incident angle and polarization. Thus, submerge in the underlying surface can be effectively distinguished by the accumulation of minor scattering. The backscattering coefficients are simulated from the Michigan microwave canopy scattering (MIMICS), and the result of simulation is consistent with the conclusion of ground-based radar scatterometer (GBRS) experiments. Finally, field experiments are carried out and the observation is used for validation.

In this work, we have explored a new method which based on Bayesian assimilation for monitoring methane (CH4) emissions at different rice phenological stages. Specifically, we investigate two algorithms, one based on the ground-based radar scatterometer (GBRS) with full polarization (HH, HV, VH, and VV) and the other based on the mechanistic process of agricultural model concluded the parameters of climate, soil and management. Experiments are conducted on conventional static box and results are compared to the outputs of Bayesian assimilation, microwave model, and Denitrification-Decomposition (DNDC) model, respectively. The result shows that the fusion has combined the tendency of mechanistic model and accuracy of microwave model. For the truth and authenticity of data and result, three criteria indicate the result with high accuracy not only agree well with the sample value but also provide high reliability on the real methane emissions process over time and space.

In order to study the coherence characteristics of low-backscattering objects such as roads and rivers, we introduce a coherence estimation approach based on clustering method. When the approach is applied to multi-temporal high-resolution TerraSAR-X images in urban areas, the results show that the coherence of roads is higher than that of rivers and shadows. This indicates that coherence features can be used to distinguish roads and water bodies. Further, this paper proposes a road detection method of synthetic aperture radar (SAR) images based on path operators and support vector machine (SVM), which combines the backscattering and coherence characteristics of the roads. Experimental results show that coherence features can be used for road detection.

This paper reports on an experiment conducted at the wind-wave tank in UESTC microwave chamber to characterize the C- and X-band radar return from water surfaces covering oil films when observed at different incidences. The measurements of Normalized Radar Cross Section (NRCS) with bi-objective calibration technique were carried out for full polarization and various wind speeds. Comparisons are performed with clean sea water and diverse oil spills. From this data set we validate the Two-Scale Model (TSM) as a calculating method to simulate the backscatter coefficients of oil spill surface. The applicability of experimental results to SAR image extraction is discussed.

This paper uses the GA-SVM inversion model to invert the suspended matter concentration in Longquan Lake. Genetic algorithm (GA) optimizes the parameters of the SVM inversion model to establish the new GA-SVM inversion model, and GA can effectively improve the efficiency and the accuracy of the SVM inversion model. The inversion model was established by using the measured hyperspectral data and suspended matter concentration. Comparing with SVM inversion model, GA-SVM inversion model achieves a better result in the application of suspended solids concentration inversion. Finally we uses GF-1 remote sensing images to retrieve suspended matter concentration in Longquan lake based on GA-SVM inversion model.

With the development of technology of digital camera, the performance of image sensor is continuously improving, which makes it possible to measure leaf area index by digital photos in a simpler and cheaper way compared with the other ways. This article proposed a new way to measure LAI by using digital images, and the formulation of measuring LAI is deduced from both the principle of LAI2000 and theory of digital camera. Considering the system error, the modified formula is also deduced. In this article, there are about 35 measurement locations which include kinds of different density of canopy, where the LAI were measured by both proposed method and professional instrument LAI2000 to validate the accuracy. The experiment results show that the correlation coefficient was 0.9794 between the proposed method and LAI2000, which proved the accuracy and feasibility of measurement of LAI based on digital photos. The new method has a bright application prospect.

Aerosol retrieval over urban areas is a difficult task because of the high reflectance of the underlying surface. In this paper, a new aerosol retrieval algorithm based on the spectral analysis of soil and vegetation from spectral library was proposed, the simulated correlation between the normalized difference vegetation index (NDVI) and the surface reflectance of red, blue bands was established. And also to solve scale problem, a conversion method based on maximizing mutual information (MI) was used. The algorithm was applied to Beijing city using the China HJ-1A/1B of the Environment and Disaster Monitoring Microsatellite Constellation Charge-Coupled Device (CCD) and MODIS NDVI data. The result, aerosol optical thickness (AOT) with a 100m×100m resolution, was compared to the ground measurement data from Aerosol Robotic Network (AERONET), which shows a high consistency with observation data, and the overall correlation coefficient of approximately 0.935 and a root-mean-square error (RMSE) of about 0.34. The algorithm is very useful and significant for environmental protection and air quality monitoring over urban areas.

The paper aimed to investigate interaction between electromagnetic wave and cereal parameters at column and row directions. S-band (center frequency 3.2GHz) and wheat had been selected as the research targets. During an entire wheat growth cycle, a series of data related to the interaction were collected for the analyses. Backscatter at row and column directions were analyzed as the function of the wheat temporal variations parameters. The research results show the dominated contribution determined the influence of wheat at row and column directions on radar backscatter. Moreover, a simple model has been presented to minimize the influence of column and row directions on radar backscatter. The research can be helpful to modeling and cereal monitoring by microwave remote sensing technology.

A classifier based on the Least Square Support Vector Machine (LS-SVM) with Fruit Fly Optimization Algorithm (FOA) for polarimetirc Synthetic Aperture Radar (SAR) image classification is proposed in this paper. This method uses pixel-based information and region-based information as the features of land cover. The former one comes from the integration of multiple polarimetric parameters obtained by various polarimetric decomposition techniques, and the latter one is derived from the Grey Level Co-occurrence Matrix (GLCM). Kernel Principal Component Analysis (KPCA) is afterwards used to reduce the dimensionality of the multi-feature data. Additionally, this method uses LS-SVM as the classifier. Due to the fact that the classification performance is dependent on the input parameters of LS-SVM, FOA is adopted to obtain the optimized input parameters. Finally, compared with the method without using FOA and the supervised Wishart method, the classification performance of a fully polarimetric SAR image is much better by using the proposed method.

This paper presents a landslide prediction method based on soil moisture estimation obtained by fully polarimetric Synthetic Aperture Radar (SAR) data and surface topography acquired by Shuttle Radar Topographic Mission (SRTM). In order to solve the problem of geometric distortion caused by topography, the study area is classified as measurable and non-measureable areas in terms of the terrain slope with respect to the SAR flight path. The polarimetric backscattering coefficients are corrected through a polarization transformation that depends on the direction of the unit normal for each image pixel. Areas of tall vegetation are excluded. A radiative transfer model for short vegetation is used for soil moisture estimation assuming the surface roughness is a fixed parameter. A soil stability model with soil moisture and slope as a parameter is employed to predict landslide. Finally, the model is applied to the Radarsat-2 images acquired from Maoxian, China, and the extracted soil moisture data is compared with Tropical Rainfall Measuring Mission (TRMM) data for validation. The soil stability model is then used for determination of areas for high possibility of landslide.

Persistent scatterers synthetic aperture radar interferometry (PSI) is a powerful remote sensing technique to detect the subsidence and landslides with an accuracy of millimeters. Distributed scatterers (DS) can be extracted to increase the measurement points with preserved Persistent scatterers (PSs), especially in non-urban areas covered with vegetation. In the experiment, we selected a set of SAR images of Radarsat-2 satellite, covered Mao country area, to detect the subsidence during the six months. An improved method has been presented to optimize measurement points for applying the InSAR technique to monitor the deformation of mountain areas through processing small scales full-polarization SAR Images.

Traditional DINSAR method is just available to obtain the deformation of no layover areas. While, for some line-shape objects located vertically over the ground, such as power transmission tower, traditional DINSAR technique can not be used to obtain the inclination displacement of it. This paper proposes an improved DINSAR (IM-DINSAR) method which can solve this problem by removing the top-down vertical height phase and obtaining the differential phase just caused by inclination displacement. The Radarsat-2 high resolution spotlight mode data are used for experiment and we obtain the inclination displacement of the towers in the study area based on this new method.

In this paper, a method for road detection based on Duda and path operators has been presented. The roads are represented as slender dark regions with constant width and reflectance in the high-resolution SAR images. The path operators (path openings and closings) were performed as morphological filters in retaining linear structures. However, the filters were not sensitive to the width of linear feature. Focused on the limitation of the method, a preprocessing procedure using Duda operators was introduced before adopting the method of morphological profiles with path operators. When the modified method was applied on RADARSAT-2 datasets for different areas, the research results show that the completeness and correctness are over 70% for road detection from SAR images.

In this paper, a new aerosol retrieval algorithm based on a method, named statistical segmentation, was proposed. Firstly, the image of Landsat 8 OLI was divided into many segments by the statistical segmentation method based on band 6 and band 7. Then, according to the characteristics of the segmentation, two ways based on the segmented results were used to get the surface reflectance. And then combined with the apparent reflectance equation and a lookup table built by 6S model, aerosol retrieval could be performed. In principle, this algorithm is based on clean pixels (almost no aerosol) at band 1 to retrieve contaminated pixels in the same segment. The retrieved results show that, compared with DDV (Dense Dark Vegetation) algorithm, this algorithm is more suitable for bright surfaces, such as urban areas.

Flood is one of serious natural disasters in the word. Synthetic aperture radar (SAR) has become a popular tool to detect the flood disaster for its distinct benefits such as retrieval of surface information, penetrability, and availability in all weathers. This paper aims to analyze microwave scattering characteristics of soil from low water content to semi-flooded status based on ground-scatterometer radar measurement. The research can demonstrate the scattering characteristic of soil at different status and be helpful to retrieve and monitor flood areas in the disaster. A regressive model combined with the radar data and the submerged proportion of soil has been presented.

The detection of ocean oil spill based on synthetic aperture radar (SAR) image has been a hot topic attracting extensive attention. In this paper, a hybrid scheme, in which we extract feature parameters and then achieve classification as follows, is presented. Two-dimensional (2-D) Otsu algorithm is applied in image segmentation process, and neural network is applied in classification course. Before image segmentation, a sort of universal processing is used, and it enables 2-D Otsu algorithm to be more applicable to SAR images of ocean oil spill.

This letter investigates the influence of row wheat for azimuthal look angles on radar backscatter at L-, S-, C-, and X-bands. The radar backscatter was collected with full polarization (HH, HV, VH, and VV) and incidence angles (10°-70°) at different wheat phenological stages. Simultaneously, wheat parameters (biomass, canopy height, stem density, leaf inclination, etc.) and soil parameters (moisture and roughness) were measured to explain the influence based on radiative transfer theory. The research results show that, when the contribution of soil scattering dominates in total backscatter, the influence of row wheat on radar backscatter is varied with the electromagnetic wavelength and the visible soil contained in radar footprint area. When volume scattering contributes more in total backscatter, the influence of row wheat on radar backscatter mainly comes from leaf parameters and wheat geometric shape. Moreover, research results also show that azimuthal look angles affect radar backscatter much mainly for variable scattering cross section and leaf parameters. The research is helpful for cereal monitoring, modeling, and cereal parameter inversion from synthetic aperture radar images.

The traditional methods for coherence optimization in the framework of multibaseline (MB) polarimetric differential interferometric synthetic aperture radar (DInSAR) applications, such as Best, MB1 equal scattering mechanism (MB1-ESM), suboptimum scattering mechanism (SOM), and exhaustive search polarimetric optimization (ESPO), all have some disadvantages. The MB2-ESM method just can be preferred for its normal accuracy and fast computing time. Signal-noise ratio optimum (SNR-OPT) in the coarse grid followed by the conjugated gradient method (SNR-OPT-CG-CGM) can be selected because of its higher accuracy and acceptable cost time. SNR-OPT has higher computational efficiency compared with ESPO because it makes the 4-D coherence optimization problem transform into two independent 2-D optimization problems (“2 + 2” optimization problem). However, SNR-OPT still costs much time. In this letter, we propose a new method which can further make this “2 + 2” optimization problem transform into one 2-D and two independent 1-D optimization problems (“ 2+1+1” optimization problem). Thus, the computational efficiency will be improved much more compared with SNR-OPT, and meanwhile, the accuracy just decreases a little. Seven full polarimetric RADARSAT-2 images are taken for experiment, and the results also show that the improved SNR-OPT-CG-CGM method is a better method considering the tradeoff between computation time and accuracy compared with other methods for DINSAR applications.

The research made an adaptation for Michigan Microwave Canopy Scattering (MIMICS) model at four bands (L, S, C, X) after taking wheat ears as the first layer. When wheat ears appears in heading stage, they locate on the top of the whole wheat and owes the different dielectric constant and water content, which should be considered the important scattering elements. The scattering character of wheat ears are fully considered in two growth stage (heading stage and ripening stage). In the process of adaptation, the stem and leaves layer was taken as the second layer instead of the trunk layer, which is different from forested environment to cereal condition. A new contribution to backscatter was inserted to compensate the total backscatter. The results after comparing the measured data and the predicted data by adaptation of MIMICS model showed the inclusion of wheat ears as one of the model components is feasible in modeling the wheat backscatter.

This paper developed a method of validation test research of domestic satellites LST products. The experimental data include high resolution landsat8 image, low resolution FY-3B and MOD11A1 LST product. The main steps of the method are as follows: (1) Three different algorithms for retrieving land surface temperature on landsat8 data. Through the comparative analysis of the results, we select an algorithm for subsequent verification. (2) Use the inversion result and MOD11A1 product to compare with FY-3B product respectively.(3) Use matlab to compute some parameters and curve data fit. The study results show that the FY-3B temperature product is similar with the others. It is available in a certain extent.

Fine registration is a fundamental step for further application of remote sensing images. Focused on deficiencies in traditional manual registration, this paper presents a new method for automatic fine registration of multi-spectral images. To make the most of image information, the algorithm detects and matches feature points in the selected bands. Then pick up the common control points which contain more reliability relative to others after eliminating wrong matching points. The last registration model can be built based on common control points and the points selected by common ones. Experimental results with Landsat TM5 images demonstrate that the method is more accurate and suitable for automatic batch processing.

A new extension method in the equal scattering mechanism (ESM) from single baseline to multibaseline (MB) for polarimetric synthetic aperture radar interferometry (PolInSAR) coherence optimization is proposed in this letter. However, despite this, this new method and the traditional available methods such as Best, ESM, MB-ESM, and suboptimum scattering mechanism have their disadvantages in the framework of differential interferometric SAR (DInSAR) applications. The ESM method cannot guarantee the global maximum in theory and just optimizes an approximation formula of the original coherence definition, which leads to a deviation. The method using exhaustive search polarimetric optimization (ESPO) must search four parameters one by one in a defined step size and cost the main computational drawback. Focusing on the disadvantage of these methods, this letter proposes another new method to transform the 4-D optimization problem into two independent 2-D problems, which costs less time than that by ESPO with basically the same accuracy. Seven full polarimetric RADARSAT-2 images are taken for experiment, and the results show that this new method has a better effect in computation time and accuracy for DInSAR applications.

Road is a great part of transportation, emergency response and disaster relief, the monitor and real-time evaluation of the state of road are always important for the rescue works after disaster. SAR has some advantages of all-weather and all-time, which can be well adapted to disaster conditions. Until now the extraction of road damage information has been studied more in optical remote sensing, while less in SAR images. Base on the researches in optical and the studies of road information extraction in SAR, we explored the road damage information extraction in this paper, and proposed a new method of road damage information extraction.

This paper developed a method of mapping wet snow and dry snow in alpine terrain based on multi-polarimetric and mono temporal ASAR data in the snow melting season.14 ASAR images were used to obtain the variation of the backscattering coefficients of snow covered areas in the snow melting process. Shi's method combining intensity and polarization property is taken as a reference to retrieve wet snow. DEM data was also used to retrieve dry snow. In order to verify the accuracy of the result, we made a scale conversion to images resulting from the ASAR data to match with the MODIS image and found an overall accuracy of 82.04%. The Google Earth image was also used as an aided verification. The results are all found to show a good estimation of the snow covered areas.

Methane is an important greenhouse gas (GHG) in the atmosphere. Conventional methods with Optical and infrared technology still have some questioned in large-area and time. With its advantage of acquiring geo-information in timely, speedy, all-day and all-weather over large areas, microwave remote sensing is one powerful approach in monitoring methane emissions to conventional methods. Ground-based scatterometer was employed to multi-wave and multi-polarization measurement to obtain the microwave scattering coefficients. The methane emission was analyzed by the microwave scattering coefficients and the parameters of the vegetation in observation area. Besides, RADARSAT-2 images are ordered to offer data support to the observation of methane emission from large-sized wetlands. Additionally, the comparison of this experiment results and former experiment results of methane emission from paddy land was done. The distinction of the two significant methane emission sources is analyzed in the paper.

Primarily the article briefly introduces the basic concept of the flat earth removal and baseline estimation, and mainly introduces the method of flat earth removal and baseline estimation based on orbit parameters using Radarsat-2 image, in detail represents the processing of flat earth removal based on orbit parameters. By removing the flat earth phase of the interferogram and analyzing the result of flat earth removal, the accuracy of generated digital elevation model and surface deformation, it shows that the method is effective, thanks to the highly precise orbit parameters. Meanwhile, the baseline estimation can be operated, which improves the efficiency.

This paper proposes a new method of spatial distribution of PM2.5concentration, which is based on Aerosol Optical Thickness (AOT) inverted by Landsat7 ETM+ Enhanced Thematic Mapper Plus (ETM+) data and PM2.5ground-based instruments. The main steps of the method are as follows:(1) Determination of dark pixels, search for the dark surface targets with the 2.2- μm channel; (2) Determination of the surface reflectance in the blue and red channels;(3) AOT retrieval, inverted from Look-up Tables (LUT) established from a set of atmospheric geometrical conditions;(4)Establish the model of PM2.5predicting with predictors and estimate the PM2.5value. The study results show that the method is an effective means for predicting the PM2.5concentration, and a beneficial supplement to the conventional ground-based measurement.

The paper proposed a soil moisture retrieval model with S-band field experimental data and volumetric water content measured in field. Focused on the problem that antenna irradiated region may not meet the minimum radar resolution pixel, the research applied the multiple independent samples measuring method and analyzed the relevance between soil moisture and backscattering coefficient of S-band VV polarization. The inversion equation obtained was applied to inverse soil moisture from SAR (Synthetic Aperture Radar) S-band images of HJ-1C (a satellite designed for environment and disaster monitoring). The inversion results were verified by the multiple independent samples data measured and agreed well with the experimental data, which shows the S-band VV polarization data can be used to monitor the soil moisture in a large scale.

A monitoring method of methane emission from rice paddies is developed by using quad-polarization radar datasets of ground-based scatterometer and space-borne RADARSAT-2. The backscattering coefficients in eight different rice growth periods are measured by using a ground-based scatterometer system, and the rice biomass, leaf-area index (LAI), water depth and CH4flux are collected by randomly sampling. Meanwhile, four scenes of RADARSAT-2 quad-polarization synthetic aperture radar (SAR) images covering the same study area are acquired. An experience rice parameter inversion model and a methane emissions model are established according to the measured data. The Models are applied to RADARSAT-2 image after pretreatment and classification to retrieve the rice biomass and the CH4flux at four dates, and come to the total methane emission, eventually. The methane emission per hectare is about 418.88 kg; it shows that methane emissions remain at a low level in the Chengdu Plain.

The paper studied the model relation of soil moisture and spectral index by using the data field measured spectral reflectance and soil moisture, and analyzed the correlation between the soil moisture and spectral index. Furthermore, based on analyzing the data measured, the research selected the sensitive band and built the optimal inversion model. The soil moisture of the studied area (Maoergai area of Minjiang upriver) was inversed, and then the inversed results were analyzed and evaluated. By analyzing the different inversed results accuracy, the research obtained the optimal method of soil moisture inversion for ecological water information index remote sensing quantitative model.

Radar image has now becoming more and more widely used in the ecological environment monitoring. The soil moisture inversion for SAR(Synthetic Aperture Radar) image has important significance in the field of agriculture and environment. However, as the SAR image data is large scale and the algorithm of soil moisture inversion is complicated, it's a time-consuming work for SAR image processing, so how to get fast processing for SAR images has now become an important problem. Therefore, the this paper use MPI(Message Passing Interface) cluster system accelerate the processing speed of algorithm, thus speeding up the SAR image processing and improving the efficiency of the use of computers. This paper designed and implemented two cluster computing modes that are master-slave mode and peer-to-peer mode. At last, we takes an experimental testing, results show that relative to master-slave mode, peer-to-peer mode can get a better acceleration effect, greatly improves the processing speed.

In this paper, the method is proposed to determine the complex permittivity of powder materials form 1 -40GHz using the transmission-line technique. In the entire frequency range, it's impractical to use only one type transmission-line filled with power materials. Therefore, 7mm coaxial transmission-line is used for 1-18GHz, regular waveguide BJ220 transmission-line is used from 17.6-26.5GHz and regular waveguide BJ320 transmission-line is used from 26.5-40GHz. Different from the measurements of solid materials in a two-port transmission-line, two Teflon solid spacers are used to house the powder materials. After obtaining the scattering parameters at the reference plane of the powder materials using an improved TRL calibration method, then reconstruct the complex permittivity based on the traditional transmission-line method. Before filling the transmission-line with power materials, the complex permittivity of air is measured as verifiable. Finally the results of the measurements of two powder materials: loess and thin sand are shown.

This paper presents a technique for the detection of damaged road in spaceborne synthetic radar (SAR) images. Roads in SAR image can be modeled as line structures, and are extracted from image by Duda detector, and the roads are accurately detected by removing line structures which aren't road information. We use a change detection algorithm based on Edgeworth approach and Kullback-Leibler divergence to obtain change information of images. Finally, we combined information of road and change detection result for detecting damaged road sections. This technique is applied on RadarSat-2 images that have a resolution of about 3m. The experimental results show that our method can detect mainly damaged road sections.

This paper presents the backscattering coefficients of rice fields using an L, S, C and X-band scatterometer system during the growth period for a rice field. The system has full-polarizations (vv, vh, hv & hh) and can view various incidence angles (0°~90°) and azimuth angles (0°~360°). The field measurements were performed in Qionglai County of Chengdu (China) for the 2009 rice growing season. The rice parameters, including biomass, leaf-area index (LAI) and canopy structure were also measured experimentally in the field. The full-polarizations backscatter measurements at L, S, C and X-band have been analyzed as a function of the incidence angle, and the temporal variations of full-polarizations at four selected 35° incidence angles have been compared with the temporal variation of rice biomass and LAI. The results indicate that the backscattering coefficients have a strong correlation with the biomass and LAI, especially in the L and S-band. Therefore, the ground-based scatterometer is an effective tool for estimating rice growth.

This paper measures backscattering coefficients of wheat fields using L, S, C, X-bands scatterometer system during a wheat growth period. The system has full-polarizations (vv, vh, hv, hh) and can view in various incidence angles (from 0° to 90°) and azimuth angles (from 0° to 360°). The wheat field locates at Qionglai County of China is measured during the 2011 growing season. From November 2010 to May 2011, twelve experimental acquisitions (ground and radar data) are carried out over the test field located in a flat area. At the same time, wheat biomass, canopy structure, LAI, soil parameters and related eco-physiological canopy variables are collected. The temporal variations of each band at selected incidence angles have been analysis during the wheat growing season. The correlation coefficients have been computed between the measured and model estimated values of σ0. The results show that the backscattering coefficient is sensitive to the biomass and LAI.

In today's society, as town roads and construction infrastructure has been improved gradually, the noise pollution has effected the environment more and more seriously. The noising map which has unique way to show the distribution of noise in the real-time and can monitor the situation of the noise effectively, it can let user master the situation of noise pollution clearly, and the application of noising map has become a main research direction of denoising work in recent years. This paper mainly analyzed the domestic and foreign research of the noising map and the main noise prediction method, selected the RLS90 model to develop the software. In the development, we used the ArcGIS Engine setups and Visual Studio programming environment, using the object-oriented development method and the GIS component second development function to develop a relatively perfect function noise prediction and noising map generation system based on the geographic information system.

This paper proposes a new method of spatial scale conversion which is combining the MODIS product geometry information with the decomposition method basing on NDVI pixel. The method makes use of the LAI ground data, high-resolution images (TM) data and MODIS pixel geometry information, which make the 30m resolution image of TM-NDVI weight up to 1km by space response function, and then we can get the value of LAI inversion after pixel decomposition. The result of the scale conversion is compared with the MODIS LAI product to achieve the validation of the low resolution (MODIS) LAI product data. The experiment results show that the new method can be fully consider of the heterogeneity of the surface and pixel correspondence to the effect of results validation, which can effectively improve the accuracy of product certification, and is much better than existing space scale method.

Hydrographic information is very important. The all-weather and penetrability capabilities of SAR make it useful for water monitoring. Previous research mainly focus on delineate the water boundary, to treat the target as water or no-water area. However, there are some places they ignored, in this paper we call it as semi-flooded area, like lakebed, areas after waters receded and semi-flooded farmland, even some special situations like ruffled water surface. Here we use the maximum between-class variance method (Otsu) combining the characteristics of water backscattering coefficient to define the targets as water, semi-flooded and no-water area in the image. The result shows this method is much better than traditional threshold classification.

Bidirectional reflectance factors (BRFs) play a key role in land surface studies. Snow has a significant influence on vegetative surface BRF. To evaluate the surface reflectance behaviors of snow-covered regions, a surface BRF database has been constructed from Multi-angle Imaging SpectroRadiometer BRF products for five biomes in the mid-high latitude regions of the U.S. (evergreen needleleaf forests, shrublands, grasslands, croplands, and urban areas). Using corresponding surface snow depth data from 26 meteorological stations, BRF signatures with snow cover are derived from the database to show the effect of snow on the BRF of vegetation. Five bidirectional reflectance distribution function models' abilities of capturing vegetation-snow mixed BRF shape are evaluated by fitting all the BRF data with snow. The results show that the Rahman model, Ross-Li model, and Walthall model perform well in fitting forest, grassland, and cropland BRFs when the surface is covered by snow. The Rahman model, Ross-Li model, and Roujean model fit visible reflectance well for mixed surfaces. The Rahman model best captures the BRF shapes, followed by the Ross-Li model.

This thesis propose an algorithm which is based on the unmanned aerial vehicle (UAV) remote sensing images geometrical rectification model with the flight attitude parameters. The algorithm can make the flight attitude information of the UAV remote sensing images for the fast geometrical distortion rectification in the course of lacking the ground control points to carry on. The quality assessment of the geometrical rectification results of the model is performed through experimental comparison and analysis. Geometrical rectification algorithm can not only obtain high resolution remote sensing images with effective rectification immediately but also improve the quality, speed and accuracy of UAV remote sensing data processing and quantified disaster information extraction.

With the ENVISAT-ASAR Alternating Polarisation mode and the parameter sets of HJ-IC-SAR as the research foundation, using the measurement data by multi-wave bands and multi-polarization ground-based scatterometer, the paper studies an inversion method of bare-surface soil moisture. The studies simultaneously consider the influence which include root-mean-square height S and correlation length L in this method, and combine the two roughness parameters, expressed as a combination of roughness Zs. Using Integral Equation Model (IEM) to analysis the relationship between Zs in different incident angles and the difference of the backscattering coefficients under two wavelengths, establishes the multinomial semi-empirical model for soil moisture inversion under different angles. The model's validation is conformed by the ground-based scattering datas, the results show that, for the medium and low roughness of the bare surface, the error is smaller than 15 percent between inversion value with the model and the measured soil moisture.

This paper explores snow bidirectional reflectance distribution function (BRDF) properties over some snow covered regions using Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging SpectroRadiometer (MISR) surface reflectance products. In the visible and near infrared (NIR) region, MODIS and MISR surface bidirectional reflectance factors (BRFs) over snow are accumulated to extract snow BRDF properties. Five surface BRDF models are concerned to simulate snow surface reflectance shape. All the models capture the distribution of snow BRFs with limit of accuracy. The simulated BRFs from several models have similar distribution trend and different details. The BRDF properties discussed can be used as background in the snow BRDF retrieval from spaceborne measurements.

This paper aims to investigate the relationship between microwave backscatter signatures of different bands and polarizations and paddy field parameters so as to accumulate data for accurate inversion of these parameters. In the entire rice-growing season we altogether carried out eight experiments in three paddy fields separately and got a great amount of data of four frequencies(L,S,C,X), and three polarizations(HH,VV,VH). A wide range of ground parameters, such as leaf area index (LAI), biomass (fresh weight), canopy height, stem density, roughness and so on, were measured periodically through the season. In this paper, analyses based on statistical correlation showed that the lower-frequency bands (L, S) VH and HH polarization were highly correlated with LAI, biomass and canopy height while VV polarization was poorly correlated. But the highest correlation coefficient with stem density was found in S band VV polarization. Polarization difference had high correlation coefficients on some unique conditions. Besides, we did some research on the matter that whether the paddy field was flooded.

Based on the remote sensing hyper-spectral reflectance measured in the Lake Kun-cheng in May, 2009 and concurrent chlorophyll-a (Chl-a) concentration assayed in the lab, a three-band model(semi-analytical method), was applied to estimate the concentration of chlorophyll-a to monitor the water quality of the lake Kun-cheng located in Chang-shu city, Jiangsu Province. Compared to the estimating results from the band ratio method based on normalized spectrum and the first derivative method, the results show that the estimating results from the three-band model was more exactly. The three-band model can improve the estimation accuracy for Chl-a by analyzing the inherent optical properties of the chlorophyll-a, the total suspended matter, the CDOM and the pure water and optimizing the choice of the three bands.

Eco-water is defined as a transformation of precipitation, which is deposited by vegetation layer, humiliated vegetation layer and soil layer. It plays an important role in the water-cycle system. As potential factors on Eco-water and Eco-water layer are different from one season to another, multi-temporal and multi-type remote sensing data, measured spectrum and the routine observation were applied to construct the indices for Eco-water and its inversion model. The four Eco-water indices, including Vegetation Canopy Interception Content, Vegetation Water Content Index, Soil Moisture Index and Eco-water Storage Index, were calculated. The results show that the RS information model can reflect the real soil moisture. The dissertation brings forward the Eco-water Remote Sensing quantitative study based on vegetation layer. The vegetation-based calculation model for Eco-water with quantitative remote rensing technology, which has been identified in the dissertation, possesses significant science affect and practical value; and it can not only advance the methods of Eco-environment study, but also promote the research on water-resources transformation and water-cycle, also enlarge the domains of remote sensing applications.

The S-band and C-band FM-CW land-based radar scatterometers were used to measure the backscattering coefficient of a variety of typical earth substances in northwest of China, including: bare soil, frozen soil, bulrush and maize etc. under the different time and different wave band and different polarized condition. First of all, the measurement principle and performance parameters of scatterometer were introduced, the detailed experimental plans and measurements specifications were developed. Based on them, the various units of measurement were completed successfully. The measurement process of scatterometer was briefly introduced in this work. according to different scattering mechanisms, features are divided into two categories: surface scattering and volume scattering, and analyzed these Scattering Characteristics and the reasons for these differences; combining with the corresponding earth substances scattering model, quantitative studying a function between the backscattering coefficient and surface parameters, getting different surface parameters of features by inversion, and analysis of a variety of influencing factors by comparing the measured datas.

The complex dielectric permittivity of pure ice is measured using the transmission/reflection method in the rectangular waveguide at frequencies between 2.6GHz and 3.9GHz and over the temperature range from -25 to -2.5°C, in order to extract the influence of temperature and frequency on the dielectric properties of ice quantitatively. The S band of microwave frequency is particularly investigated because of its importance and specialty. The experiments in this study show that the real part of the complex permittivity of pure ice is around 3.16, independent of frequency. The imaginary part changes over the frequency with the nonlinear function and the special frequency point 3.3GHz is found. Two polynomial functions of temperature are selected for the real part and the imaginary part, respectively. The real part increases firstly and then decreases with the increasing temperature, while the imaginary part just monotonously increases with the increasing temperature.

Leaf area index (LAI) is an important parameter for describing vegetation canopy structure in the terrestrial ecosystem on the global, continental and regional scales. In this paper, a simple data assimilation method for improving estimation of MODIS LAI time-series data products based on a new 2-D LMS (two-dimensional least mean square) adaptive filter was proposed. Firstly, The new 2-D LMS adaptive filter algorithm is introduced and analyzed. Secondly, A simple data assimilation method for improving estimation of MODIS LAI time-series data products based on the new 2-D LMS adaptive filter and quality control data of MODIS LAI is proposed. Finally, the experiments are performed based on the simple data assimilation method using MODIS LAI data products from 2000 to 2005 of southwestern China.