Nonparametric statistics methods are a class of robust and potent machine learning operators, which are widely used in various domains such as finance, medicine, and computer science. Such methods deliver an accurate estimation without an assumed data distribution. Moreover, they can handle discrete data with various data sources. Despite their desirable features, the calculation of large-scale nonparametric statistics is both compute- and memory-intensive, and the performance overhead hinders them from widespread usage.

Unmanned aerial vehicles (UAV) has been increasingly popular in the fields of crop growth monitoring, with their practical advantages, such as their ability of low-cost, rapid and repetitive capture. LAI is a key indicator for evaluating crop population growth and canopy structures and its accurate measurements would be of significance in the modern precision agriculture research. Therefore, we develop a peanut leaf area index estimation method based on fusion of texture and spectral information from UAV multispectral imagery. In our work, we compared the performance of LAI estimation using spectral and textural characteristics, and explore the potential of integrating them for estimating peanut LAI. Following this, LAI estimation models are constructed using spectral, textural characteristics, and a combination of spectral and textural characteristics based on some frequently-used statistical models. The results indicate that compared with only spectral or textural characteristics, fusion of both achieves better accuracy, which suggests that the texture features offer the critical information for improving the peanut LAI estimation accuracy. In addition, in order to select the best model, we compared four machine learning models and found that Support Vector Regression (SVR) is an optimal approach for the peanut LAI estimation, with higher R2(=0.817) and lower RMSE (=0.639) values for different feature sets. What’s more, the key parameters for computing texture features would affect the estimation performance, where the fluctuation is slight from the grayscale. Our observations would have important inspirations for the statistical model-based science discovery in the field phenotyping inversion.

The surface reflectance product is the most crucial and fundamental quantitative product in optical remote sensing, serving as the source for various land surface parameter products. This study initially conducts the retrieval of aerosol optical depth (AOD) from GF1/WFV data. The 6SV model, combined with dark object method and histogram matching, is utilized to achieve AOD inversion at a 16-meter resolution on a per-pixel basis. Subsequently, atmospheric correction is performed using the radiative transfer equation to obtain surface reflectance. The culmination of our efforts involved a comprehensive quantification of uncertainties throughout the production process of the proposed surface reflectance product, allowing for a robust assessment of its reliability, accuracy, and applicability. A thorough evaluation was conducted to ascertain the efficacy and potential of the proposed methodology.

The centers of matching craters can be beneficial additions to the control point database. Crater matching on heterogeneous surfaces is helpful in testing its applicability to the entire moon. Therefore, we propose a coarse-to-fine crater matching method for heterogenous surfaces on images acquired from the narrow angle camera (NAC) of the lunar reconnaissance orbiter camera (LROC) and the Chang’e-2 digital orthophoto map (DOM). First, we perform coarse matching based on the Hausdorff distance using the area and coordinates of the crater. Then, the mismatched craters are removed by using the affine transform model fitted by corresponding points of mutual information matching. Finally, we use the retained matched crater centers to fit the affine transformation model between the images, predict the corresponding position, and obtain the corresponding crater around it to achieve fine matching. The results show that the proposed method obtains numerous crater matches on images covering different terrains and solar altitude angles compared to the Hausdorff distance-based crater matching method. For the five experimental scenes registered using matched craters, the mean values of the checkpoints are approximately 2 and 3 pixels for scenes with small and large differences from Chang’e-2 solar altitude angles, respectively, and the standard deviations (STDs) for both are approximately 1 pixel. In addition, the highlands have lower accuracy than the maria, with a variance of less than 1 pixel. Furthermore, the registration accuracy is related to the diameter and number of craters.

Accurately obtaining information of road traffic conditions is of great significance to people’s travel planning and arrangement of social shared resources, and has become a major research focus in the field of smart cities. Accurately predicting road conditions poses a huge challenge due to the complex spatial correlations and nonlinear temporal dependencies of real-time traffic networks. In this paper we propose a Multi-Range Attention-Based Dynamic Graph Convolutional Network (MRA-DGCN) to model complex traffic networks. The MRA-DGCN model uses a bicomponent modules to separate different periodicity to extract refined traffic signal. In the MRA-DGCN model, we use the adaptive spatial-temporal network block (ASTnet block), which includes dynamic graph convolution and temporal attention, to mine complex spatial correlations and nonlinear temporal dependencies, respectively. In the adaptive spatial-temporal network block, we use dynamically generated adjacency matrices instead of existing distance-based adjacency matrices to perform graph convolution operations to aggregate information between nodes during model training. Instead of hierarchically extracting spatial-temporal signal, we adopt temporal attention to capture the spatial-temporal information synchronously to improve the prediction performance. Furthermore, we propose a residual gated network to control the flow of information passed to the next hidden layer to enhance the predictive accuracy. Extensive experiments on two real-world traffic datasets, METR-LA and PeMS-BAY, show that the MRA-DGCN achieves the state-of-the-art results.

The reconstruction of 3-D models of power pylons from light detection and ranging (LiDAR) data plays an important role in power transmission safety. However, accurate reconstruction of power pylon models still faces challenges, e.g., complex structures, missing data, and occlusion. In this letter, a novel four-component segmentation method is proposed for reconstructing power pylon models. In the proposed method, the pylon components of the pylon head, pylon body, cross-arms, and pedestal are first defined in terms of the common features and functionality of each component. Then, these four components are each segmented and identified based on their position and shape features from the raw point cloud. An improved approach based on Metropolis–Hastings sampling and a simulated annealing algorithm is proposed to estimate the model parameters. Based on the estimated parameters, the 3-D shapes of the individual components are reconstructed and stacked to form the whole pylon model. Experimental results show that our methods are able to reconstruct pylons with complex-shaped heads and multiple cross-arms, with an average reconstruction error of less than 0.3 m. Compared with the standard Metropolis–Hastings algorithm with annealing, the parameter estimation process in our strategy improves the computational efficiency by 7.54%.

Rocks are one of the major surface features of Mars. The accurate characterization of the chemical and mineralogical composition of Martian rocks would yield significant evolutionary information about relevant geological processes and exobiological exploration. Many existing rock recognition systems generally assume that all testing classes are known during training. Over real planetary surfaces, the autonomous recognition system is likely to encounter an unknown category of rock that is crucial to the performance of the rock classification task. Therefore, we develop an open-set Martian rock-type classification framework based on their spectral signatures, with the subgoal of new/unknown rock-type recognition and category-incremental learning for expanding the recognition model. First, the spectral signatures of rock samples are captured to characterize their mineralogical compositions and physical properties, which serves as the input of the developed framework. To further produce the highly discriminative feature representation from the original spectral signatures, a Transformer architecture integrated with contrastive learning is constructed and trained in an end-to-end manner to force instances of the same class to remain close-by while pushing those of a dissimilar class farther apart. Following this, according to the extreme value theorem (EVT), category-specific distance distribution analysis is conducted to detect and identify new/unknown types of rock samples due to the isolated characteristics of new/unknown rock samples in the latent feature space. Finally, the recognition model is incrementally updated to learn these identified "unknown" samples without forgetting previously known categories when the associated labels are progressively obtained. The multispectral camera, a duplicated payload of the counterpart onboard the Zhurong rover, is used as the multispectral sensor for capturing the spectral information of the laboratory rock dataset shared by the National Mineral Rock and Fossil Specimens Resource Center for both qualitative and quantitative evaluation. Experimental results indicate the effectiveness and robustness of the developed in situ analysis framework.

Airborne light detection and ranging (LiDAR) point clouds have become growingly popular as a reliable data source for 3-D digital building model reconstruction. Therefore, we develop a label-constraint approach for automatically detecting building roofs using airborne LiDAR point clouds and multispectral images, where the label information is introduced in both the discriminative feature space generation and the detection procedure. To obtain a robust and highly discriminative descriptor, a supervised sparse coding-enhanced bag of visual word (SC-BOVW) model based on a learned discriminative dictionary is used to encode local geometric and spectral information within each super-voxel into high-level semantic representation, which is then fed into a support vector machine (SVM) classifier for distinguishing buildings from others. Additionally, a graph cut-based procedure is used as a postprocessing step to guarantee the spatial consistency in detection results. Experiments were conducted on the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark data sets. Results indicate that the proposed method is accurate and efficient in terms of building roof region detection. Moreover, the proposed method is superior to other existing methods with average differences in recall of 2.23%, precision of 0.28% and quality of 1.99%.

Global geo-reference Lunar Reconnaissance Orbiter Camera-wide angle camera (LROC-WAC) mosaic imagery provides the precise geographic information for the mapping of mineral elements based on Chang'E-1 interferometric imaging spectrometer (IIM) imagery. However, the traditional image registration methods fail to achieve the accurate registration in between due to heterogeneous characteristics. Therefore, this letter proposes a semiautomatic registration method for Chang'E-1 IIM imagery based on global geo-reference LROC-WAC mosaic imagery. Due to the lack of ground control points, the method implemented a random sample consensus (RANSAC)-guided affine transformation (AT) model to help predict the potential coarse correspondence. Afterward, a multiwindow image matching approach is performed for the fine correspondence. To verify the performance of the proposed method, experiments were performed using Chang'E-1 IIM imagery and geo-reference LROC-WAC mosaic imagery. Experimental results indicate that the proposed method can obtain a massive number of homologous points while minimizing manual intervention, which is comparable to manual results and has high image registration accuracy.

Point cloud feature extraction as a classification task is crucial in maximizing the efficient downstream applicability of raw point clouds. With the goal of learning optimum features for efficient classification of a multi-class point cloud for downstream applications, this letter presents a supervoxelFisher vector (FV)-based approach for airborne light detection and ranging (LiDAR) data classification. In our approach, FV encoding is implemented to deduce compact global descriptors from aggregated supervoxels to establish a more descriptive and discriminative representation, transforming the low-level visual features into high-level semantic features. As a result, the proposed approach combines local and global feature properties through the quantization and aggregation of higher order statistics to harnesses their combined advantages for producing good classification results. Experiments were conducted on the international society for photogrammetry and remote sensing 3-D semantic labeling benchmark data set. Results indicate that the proposed approach is robust and efficient, attained the third best position with an overall accuracy of 81.79%, and ranked first with an F1-score of 72.31%.

The pole-like object detection is of significance for robot navigation, autonomous driving, road infrastructure inventory, and detailed 3-D map generation. In this letter, we develop a skeleton-based hierarchical method for automatic detection of pole-like objects from mobile LiDAR point clouds. First, coarse extraction of building facades is adopted for the occlusion analysis. Second, slice-based Euclidean clustering algorithm is implemented to derive a set of pole-like object candidates. Third, skeleton-based principal component analysis shape recognition is presented to robustly locate all possible positions of pole-like objects. Finally, a Voronoi-constrained vertical region growing algorithm is proposed to adaptively producing the individual pole-like objects. Experiments were conducted on the public Paris-Lille-3-D data set. Experimental results demonstrate that the proposed method is robust and efficient for extracting the pole-like objects, with average quality of 90.43%. Furthermore, the proposed method outperforms other existing methods, especially for detecting pole-like objects with a large radius.

Lunar impact craters form the basis for lunar geological stratigraphy, and small-scale craters further enrich the basic statistical data for the estimation of local geological ages. Thus, the extraction of lunar impact craters is an important branch of modern planetary studies. However, few studies have reported on the extraction of small-scale craters. Therefore, this paper proposes a coarse-to-fine resolution method to automatically extract small-scale impact craters from charge-coupled device (CCD) images using histogram of oriented gradient (HOG) features and a support vector machine (SVM) classifier. First, large-scale craters are extracted as samples from the Chang'E-1 images with spatial resolutions of 120 m. The SVM classifier is then employed to establish the criteria for classifying craters and noncraters from the HOG features of the extracted samples. The criteria are then used to extract small-scale craters from higher resolution Chang'E-2 CCD images with spatial resolutions of 1.4, 7, and 50 m. The sample database is updated with the newly extracted small-scale craters for the purpose of the progressive optimization of the extraction. The proposed method is tested on both simulated images and multiple resolutions of real CCD images acquired by the Chang'E orbiters and provides high accuracy results in the extraction of the small-scale impact craters, the smallest of which is 20 m.

The microwave signal at L-band is very sensitive to the soil moisture due to its penetrability. To analyze an algorithm to retrieve soil moisture at L-band, a simulation of microwave brightness temperature is conducted by using the τ-ω model in this study, and two methods of resampling are compared. One of the brightness temperature simulation is based on the point observation on ground, and the other is from the ground observation data of 1km resolution resampled from point. It turns out the latter method has a smaller error. An airborne L-band data from a Polarimetric L-band Microwave Radiometer (PLMR) acquired during the Hiwater experiment held in the Heihe River Basin in 2012 are used to validate the brightness temperature simulation. And the root-mean-square errors between L-MEB simulated and PLMR are 9K to 12K for V-polarization, and 6K to 8K at H-polarization respectively at different angles.

Passive microwave remote sensing observations have been widely used for long-term global monitoring of the Earth. Passive microwave data can be utilized to obtain important parameters (e.g. precipitation, snow cover, sea ice and soil moisture) of the Earth system with relatively high temporal resolution and regardless of lighting and cloud conditions. However, due to the limited lifetime of individual satellite sensors it is necessary to examine and cross-calibrate the brightness temperature data of different instruments when establishing long-term time series of observations. In this paper, brightness temperature data from SSMIS, AMSR-E and MWRI were compared over the Greenland ice sheet. In addition, the brightness temperature data from these instruments were also compared against tower-based brightness temperature observations over a test site in the boreal forest zone. A simple Snow Water Equivalent (SWE) retrieval algorithm was applied to the three satellite data sources to investigate the effect of observational biases to a typical satellite product on snow cover.

Surface soil moisture is an essential variable in hydrological process. A physically based statistical methodology for surface soil moisture retrieval in the SNOTEL-770 station was examined in this study. This approach uses MVIs-B parameter to minimize the vegetation effects. And by adding the weighted emissivity at two polarizations, the surface roughness effects are eliminated. Considering the noisy behavior of MVI-B might limit its applications, in this study, we attempted to use the Fourier analysis to refine the MVI. The methodology was tested against the SNOTEL-770 station with experimental data sets collected from Climate Change Initiative (CCI) Soil Moisture project and was shown to be an effective method of soil moisture retrieval for areas with sparse vegetation coverage.

A new dielectric model for vegetation in frozen environment based on the Debye-Cole dual-dispersion model was already developed in part I. This model can be used at a wide frequency range (0.5GHz - 40GHz) and even applicable for negative temperatures reached -20°C. In this paper, a matrix-doubling microwave emission model was used to evaluate vegetation effects in a frozen environment at 6.925, 10.65, 18.7 and 36.5GHz (V and H polarization). To verify the new developed dielectric model, a kind of young tree named Populus tomentosas was measured based on the Truck-mounted Multi-frequency Microwave Radiometer in December of 2012. In the experiment, the ground was irrigated to get rid of the soil signals. Also, the row-structure's effect on the trees can be eliminated when water covered the whole ground surface. Comparisons and analysis between model simulations and field measurements showed the dielectric model can be applied to microwave emission model as input data. Furthermore, the characteristics of microwave radiation of vegetation in frozen environment were evaluated and how the vegetation dielectric constant affected the electric field and physical property of vegetation layer was explained.

Snow cover information is crucial to global climate change research and hydrological applications. Snow cover over the Tibetan Plateau is important to water resources and Asian climate. Based on high temporal resolution of geostationary satellite data, snow cover map with less cloud obscuration can be obtained daily. In this paper, geostationary meteorological satellite FY2E VISSR data is used to obtain the snow cover information over the Tibetan Plateau in year 2010 and 2011 winter seasons. Meteorological station observations are used to evaluate the performance of snow cover maps. In addition, MODIS and IMS snow cover products are used for comparison and validation. Results indicate VISSR snow cover maps show good performance in reducing cloud obscuration. MODIS snow cover maps present highest overall accuracy, followed by VISSR and IMS. VISSR and IMS snow cover maps show slight over-estimation of snow cover over the Tibetan Plateau.

In this study, a parameterized first-order radiative transfer (RT) model for short vegetation layer is employed to retrieve the single scattering albedo of winter wheat by combining passive microwave AMSR-E data with optical MODIS data. The microwave vegetation indices (MVIs) with two adjacent frequencies of AMSR-E at H/V polarization derived from the parameterized model are used to cancel out the ground surface emission signals. Then a simulating database based on field measured parameters is established to figure out the relationship of the optical thickness, single scattering albedo at C and X band, respectively. Finally the characteristics of retrieved single scattering albedo are analyzed and the daily NDVI was utilized for evaluating the retrieved results.

Snow cover is an important variable for global climate change research and hydrological application. Recent years, the snowfall events in southern China indicate the limitation of using optical or passive microwave remote sensing respectively. In this paper, China's first generation of geostationary meteorological satellite Fengyun-2E and the second generation of polar-orbit meteorological satellite Fengyun-3B are used to monitor snow cover in China from Jan 1 to 31, 2011. In order to monitor snow cover in real time and make use of China's meteorological satellites, FY-2E VISSR and FY-3B MWRI are mainly used. AMSR-E is also used, since MWRI can't completely cover China daily. The main purpose of this study is to propose an effective method of monitoring snow cover daily mainly using China's meteorological satellites.