Quad polarimetric (quad-pol) Synthetic Aperture Radar (SAR) has unique advantages to record the detailed backscattering information, which significantly enhances the interpretation quality of objects. However, the quad-pol SAR system’s intricate nature imposes constraints on its swath coverage and spatial resolution. A promising solution to this challenge lies in the recovery of quad-pol information from Compact-Polarimetric (CP) SAR data. In this paper, recovery methods based on the convolutional neural network (CNN) are introduced, for both single-look complex (SLC) and multi-look complex (MLC) SAR data. It also presents a comparative performance analysis of these recovery techniques across two data types, emphasizing the feasibility and efficiency of two approaches in overcoming the limitations of quad-pol SAR systems while maintaining high data quality.

Multibaseline interferometric synthetic aperture radar (InSAR), multibaseline polarimetric InSAR (PolInSAR), and SAR tomography (TomoSAR) are the advanced techniques for forest height inversion. However, accurate large-scale inversion using these techniques still faces two key problems: 1) for InSAR and PolInSAR, the existing inversion models fail to accurately describe the vertical structure, reducing the inversion performance; 2) for TomoSAR, its baseline configuration requirement is too high to reconstruct the vertical structure of the large-scale forested area. To solve these two problems, this paper proposes a high inversion accuracy forest vertical structure heterogeneity (FVSH) coherent scattering model and a small-scale tomography-aided multibaseline (Pol)InSAR (STMI) forest height inversion framework, which provide a synergic observation and inversion scheme of these multibaseline SAR techniques using machine learning approaches. The different-frequency InSAR and PolInSAR data acquired above the different types of forested areas are selected for validation. The experimental results show the effectiveness of the proposed framework.

Synthetic Aperture Radar (SAR) exhibits the capacity for comprehensive and continuous Earth observation, regardless of weather conditions and diurnal variations. Contemporary methodologies for rice field identification utilizing SAR rely upon the entirety of the rice growth cycle data, posing challenges in discerning rice cultivation within the ongoing cultivation cycle. In addressing this challenge, the present study introduces a novel metric, namely the 3-Sigmoid index (SSSI), designed to quantify the early season probability of land parcels planted rice. SSSI fully uses the crucial feature of intensity variation from low to high backscatter based on time series SAR data as paddy fields progress in their growth stages, so that we can identify in-season rice early. The method was validated in two experimental areas, and the experimental results indicate that the approach exhibits heigh accuracy in early-stage identification. Moreover, the method demonstrated successful recognition of rice fields during the tillering phase and even prior to it. In addition, the SSSI is independence from requisite prior knowledge, reference samples, and a plethora of pre-established parameters. This characteristic underscores its potential for widespread and extensive practical applications, particularly in regions characterized by persistent cloud cover, where optical remote sensing data is frequently inaccessible.

Monitoring rice growth from the space has aroused the interests of researchers from all over the world. Previous researches about rice phenology focused on dividing paddy rice into different phenological intervals, without knowing the specific date when a certain phenological stage starts. In this paper, different characteristics of the start of three important phenological stages are analyzed. A set of C-band SAR images acquired by SENTINEL-1 has been used to retrieve the start of three key phenological stages (leaf development, stem elongation and inflorescence emergence) of paddy rice. A dynamic time warping method was applied for the alignment of SAR time series curves, which are constructed using backscattering coefficients of different polarimetric channels (VH, VV and VH/VV ratio). Results show that all three kinds of time series curves show great potential in detecting start of leaf development of rice and VH/VV curves perform better in recognizing start of inflorescence emergence.

Nowadays, non-corner reflectors (CRs) calibration techniques are attractive to relieve the workload of polarimetric calibration. Yet, it is challenging to derive the complex co-pol channel imbalance (CCI) precisely and conveniently when CRs are unavailable. Our previous research provided the general unitary zero helix (gUZH) and phase-optimized UZH (pUZH) methods to estimate CCI by the bare soil pixels. However, the amplitude is still overestimated compared with CRs. Thus, this paper proposes to optimize the goal function by L2 normalization and derive the amplitude-optimized UZH (aUZH) to estimate the CCI amplitude robustly. The new aUZH performs well in resisting errors from improper reference picking. We also develop a signal-to-noise ratio (SNR) filter that selects the soil pixels with high SNR into aUZH to reduce the influence of the noise floor. Furthermore, we combine aUZH and the SNR filter to develop a method, i.e., HybridC, to process the massive data for a more precise solution. This paper validates the new algorithm through the external calibration of the Gaofen-3 satellite from 2017 to 2020. The result shows that CR error in HH/VV is better than 0.26 dB after aUZH calibration. Furthermore, we process the proposed HybridC as a tool to monitor the sensor quality of the Gaofen-3 in over 40,000 images. We find that the imbalance phase exceeds the designed specification at some beams and that our algorithm can calibrate the bias precisely.

Hyperspectral/multispectral and panchromatic of optical remote sensing images are commonly used for multisensor image fusion, which has been applied in various applications of Earth observation. However, the utilization of optical remote sensing data suffers from the limitation of bad weather and cloud contamination. To address aforementioned issue and enhance spatial details of polarimetric synthetic aperture radar (PolSAR) image, a novel dual-domain super-resolution image fusion method is proposed by combining improved spherically invariant random vector (ISIRV) model with generalized adaptive linear combination approximation (GALCA) technology in this study. The proposed method decomposes the task of image fusion into polarimetric and texture domain fusion by integrating polarimetric components of PolSAR image and texture detail component of panchromatic image, which can significantly improve spatial resolutions of the PolSAR image while preserving polarimetric information. The data fusion experiment is implemented with three data sets including panchromatic images of Gaofen-1 (GF-1) and Gaofen-2 (GF-2) and the quad-pol SAR data of Gaofen-3 (GF-3) and Radarsat-2. Results show that the proposed dual-domain image fusion method provides a better performance compared with state-of-the-art multisensor fusion methods (BT, PCA, GS, indusion, and PRACS) regarding qualitative and quantitative evaluations. In addition, results of image fusion are applied to image classification over agricultural and urban areas of China, which shows that classification accuracy is significantly improved when compared with the result using only the original image.

The deep convolutional neural network (CNN) has been extensively applied to polarimetric synthetic radar (PolSAR) imagery classification. However, its success is greatly dependent on numerous labeled samples for revealing and modeling the characteristics of different targets, thus remaining a challenge in maintaining high accuracy in limited sample cases. To address this issue, a deep reinforcement learning (RL)-based PolSAR image classification framework, named deep Q-fully CNN (DQFCN), is proposed in this article. In this framework, two ways are adopted to boost the classification performance while reducing training samples. On the information utilization hand, the spatial neighboring information and polarimetric decomposition information of PolSAR data are both extracted to enrich the feature representation of the sample. Meanwhile, the 3-D CNN architecture is adopted to learn the spatial-polarimetric jointed characteristics simultaneously. On the model learning hand, two RL learning strategies are employed to promote classification performance. The first one is learning from scratch, which does not use any label information as prior knowledge but learns from its self-generated experience. Learning from pretraining is the second strategy in which the networks are sequentially trained from labeled samples and experience data to reduce the time cost. As far as we know, it is the first time that an RL-based fully CNN has been proposed for PolSAR image classification. Experiments on three benchmark datasets prove the effectiveness of the proposed framework, suggesting that the two adopted strategies achieve boosted performance in all experiments, particularly in a limited sample size.

The Chinese Gaofen-3 satellite currently provides us with an open way to access fully polarimetric data in the C-band frequency. The noise equivalent sigma zero (NESZ) is a crucial factor when calibrating the additive noise in radar imagery. For most radar sensors, NESZ coefficients are stored in header files, but these are not provided for the Gaofen-3 products. The minimum eigenvalue estimator (MEE) and maximum likelihood estimator (MLE) are the two most common techniques used to derive the NESZ from polarimetric imagery. Nevertheless, the bias has been found to be higher than 5 dB compared with the noise measurement circuit (NMC) of the hardware. In this article, we propose a minimum noise envelope estimator (MNEE) for the robust estimation of the Gaofen-3 NESZ. In this article, we carried out an in-depth investigation to analyze the error sources of the MEE and MLE techniques. Based on our analysis, the MNEE framework requires the use of the ocean surface as a reference, and MNEE is combined with the minimum operation to suppress overestimation. In the experimental section, we describe how we validated the proposed algorithm with Radarsat-2 images, and the MNEE is treated as a tool to estimate the NESZ of Gaofen-3 polarimetric products. We found that the Gaofen-3 NESZ is generally less than -20 dB, which satisfies the design specification. The range-dependent NESZ coefficients are provided here to allow convenient noise correction for Gaofen-3 data users.

For forest with complex structure, the vertical structure backscatter is influenced by a combination of factors, including the frequencies of the radar waves and the forest biophysical parameters (i.e., density, species). The backscatter-induced error is thus a critical element in limiting the accuracy of polarimetric synthetic aperture radar interferometry (Pol-InSAR) forest height inversion based on a single model. It is, therefore, necessary to select the optimal backscatter profile from the multiple possible solutions in each pixel of the test site. In this letter, the impacts of backscatter in forest height estimation based on the models of random volume over ground (RVoG) (σ > 0), RVoG (σ <; 0), and Gaussian vertical backscatter (GVB) were investigated in the complex plane, and then with the combined use of Pol-InSAR and light detection and ranging (LiDAR), a random forest (RF) classifier is trained to obtain the optimal backscatter function and Pol-InSAR forest height from the results based on the different models in each pixel. The proposed method was tested with single- and multi baseline Pol-InSAR data in the P-band, and the root-mean-square errors (RMSEs) of the proposed approach were 2.85 and 2.69 m, respectively, which represented average improvements of 20.6% and 17.7% over the optimal single-model inversion.

In this article, we propose a polarimetric calibration (PolCal) algorithm to estimate the system crosstalk, cross-polarization (x-pol), and co-polarization (co-pol) channel imbalance (CI) when ground corner reflectors (CRs) are unavailable. The current PolCal process requires at least one trihedral CR to determine the co-pol CI. However, the deployment of ground CRs is costly and may even be impossible in some areas. To calibrate a polarimetric image without CRs, our proposed method automatically extracts the volume-dominated and Bragg-like pixels as a reference to estimate the crosstalk, x-pol, and co-pol CI values. Then, a first-order polynomial model is exploited to fit the co-pol CI to further improve calibration accuracy. In the experimental section, we demonstrate the effectiveness of our proposed method with data from two of China's newly developed very high-resolution systems. The experiments confirmed that the proposed workflow can be considered as a feasible calibration scheme when the ground deployment of CRs is impossible, and it is also an effective analysis tool for the assessment of calibrated products.

The quick and accurate extraction of water bodies from images is imperative for land resources management, ecological protection, and flood disaster prevention. However, the prevalent methods for water body extraction of SAR imagery have the major issues of depending on expert experience, the poor retention of water-land boundaries, and a high false alarm rate. In this paper, focusing on these issues, we study the effectiveness of water body extraction using only simple polarimetric decomposition components and commonly used machine learning classifiers for Gaofen-3(GF-3) image. We test it using different sizes of training samples and compare it with the previous methods. The experimental results showed that the combination of polarimetric decomposition components and machine learning classifiers can achieve satisfactory accuracies in water body extraction of GF-3 image, and has strong robustness, reliability and high application value.

A modified model-based polarimetric decomposition method, considering both the surface and dihedral scattering depolarization, is proposed for soil moisture retrieval. In the parameter solution, it works combining at least two polarimetric SAR images acquired simultaneously in different incidence angles. Moreover, it needs not to decide whether dihedral or surface scatter is the dominant contribution. The experiments to demonstrate the potential of the proposed approach is carried out using L-band polarimetric UAVSAR multi-incidence data in Winnipeg, Canada. The scattering mechanism of forest, grass land, urban, and barren area are analyzed compared with Yamaguchi three-component decomposition results. The performance of the soil moisture estimation algorithm is also assessed by comparing the retrieval results with in situ measurements.

This letter presents two main contributions to the data quality assessment of China's new GaoFen-3 radar satellite. First, we observed a half-pixel misregistration between the horizontal (H) and vertical (V) transmitting channels in the azimuth direction. This was determined by investigating the corner reflector (CR) response of some stripmap products of GaoFen-3 quad-pol stripmap (QPSI) mode. Second, to check whether the azimuth misregistration exists in different beams of QPSI mode, we improved the RADARSAT-2 channel checking method as a tool and evaluated more than 300 stripmap scenes. This letter confirms that the half-pixel misregistration problem, which can cause about 10% decoherence in the co-pol and cross-pol channel correlation coefficients, is common in the GaoFen-3 stripmap products of QPSI mode. Furthermore, the improved method can be considered as an effective way to fix the misregistration problem.

Road detection and centerline extraction from very high-resolution (VHR) remote sensing imagery are of great significance in various practical applications. Road detection and centerline extraction operations depend on each other, to a certain extent. The road detection constrains the appearance of the centerline, and the centerline enhances the linear features of the road detection. However, most of the previous works have addressed these two tasks separately and have not considered the symbiotic relationship between them, making it difficult to obtain smooth and complete roads. In this paper, a novel multi-scale and multi-task deep learning framework for automatic road extraction (MSMT-RE) is proposed to build the relationship between them and simultaneously complete the road detection and centerline extraction tasks. U-Net is selected as the basic network for multi-task learning due to its strong ability to preserve spatial details. Multi-scale feature integration is also applied in the framework to increase the robustness of the feature extraction. Meanwhile, an adaptive loss function is introduced to solve the problems of roads taking up a small percentage of the training samples, and the fact that the positive samples of the two tasks are unbalanced. Finally, experiments were conducted on two public road data sets and two large images from Google Earth, and the proposed framework was compared with other state-of-the-art deep learning-based road extraction methods, both quantitatively and qualitatively. The proposed approach outperformed all the compared methods, confirming its advantages in automatic road extraction.

This paper presents a global weighted least-squares (GWLS) optimization framework for polarimetric synthetic aperture radar (PolSAR) despeckling. GWLS is simpler than other optimization methods because it does not lead to complex optimization and iterative convergence problems. Solving the PolSAR despeckling problem based on GWLS is equivalent to solving nine sparse linear systems. First, the guidance image is constructed by the span image of the PolSAR data to calculate the five-point spatially inhomogeneous Laplacian matrix. Next, the weighted sum of the Laplacian matrix and an identity matrix is used to construct a coefficient matrix for the nine linear systems. Finally, each speckle-free channel of PolSAR data is reconstructed equally and globally by solving the linear systems with the same coefficient matrix. Filtering each element of the coherency matrix equally preserves the scattering property inherent in PolSAR data. The performance of the GWLS-based method is demonstrated by both simulated and real PolSAR data. Refined Lee filter, intensity-driven adaptive-neighborhood, improved sigma filter, and nonlocal pretest filter are used in qualitative and quantitative comparison. The experiments show that the proposed method can reach a good tradeoff between noise suppressing and detail preservation and has relatively high processing efficiency.

This paper presents a spherically invariant random vector (SIRV)-based dual-domain filter for polarimetric synthetic aperture radar (PolSAR) speckle suppression. The SIRV model is a simple clutter decomposition model designed for heterogeneous scenes that decomposes the clutter into two independent domains: the texture domain and the polarimetric domain (also known as the speckle domain). The dual-domain filter takes full advantage of the SIRV model to decompose the task of PolSAR speckle suppression into polarimetric domain filtering and texture domain filtering. The polarimetric domain filtering involves estimating a stable state of the normalized coherency matrix with similar samples via fixed-point iteration. For this purpose, the nonlocal patch matching distance measure and the cutoff threshold in the SIRV model case are defined to select similar samples. The texture domain filtering involves reconstructing a sparse texture image without the effect of speckle. For this purpose, patch ordering-based SAR image despeckling via transform-domain filtering (SAR-POTDF), which is based on simultaneous sparse coding (SSC), is applied for the texture filtering. After the dual-domain filtering, a speckle-free PolSAR image can be reconstructed by the product of the texture and the normalized coherency matrix. The effectiveness and robustness of the proposed method was demonstrated by experiments undertaken with CETC-38 X-band, DLR F-SAR S-band, and IECAS C-band high-resolution PolSAR images. The DLR E-SAR L-band medium-resolution data were also used for comparison. The results showed that the dual-domain filtering is effective for the speckle suppression of high-resolution PolSAR images with heterogeneous and detailed scenes.

The periodic surface are often seen in agriculture, such as the planting fields of potato, sugarcane, and green onion. There are strong coherent scattering for the fields whose aligning direction is perpendicular to radar's light of sight (LOS). Enhanced backscattering coefficients induced by coherent scattering on synthetic aperture radar (SAR) images hinder the retrieval of soil moisture. The paper investigated the capability of different retrieval models in the inversion of soil moisture for bare periodic fields. The results show that the copolarized ratio and HV backscattering coefficient, which are less affected by the periodic structure, can be used to reduce the effect of coherent scattering on soil moisture inversion.

The main objective of this paper is to evaluate a time series of Sentinel-1A soil moisture product acquired from March 2015 to November 2016 using global passive ones: SMAP, SMOS and AMSR-2, together with ground measurements at ISMN site Dahra in Senegal. The retrieving algorithm is a LUT method based on simulated data cube using MIMICS and AIEM of VV polarization. Results show that ubRMSD and MAD of soil moisture estimated by 1km Sentinel-1A compared to ground measurements are both below 6%, and most of Pearson correlations between Sentinel-1A and global products are significant, which indicate the great potential of the application of Sentinel-1 data. Comparisons among the global products are also conducted to embody that L band is indeed more suitable for soil moisture estimation than C band.

Recently, with the rapid development of earth observation (EO) techniques, the similar object information has been acquired in different regions, by the use of various sensors. It brings up a new challenge, that is, how to identify cross-domain objects. To cope with this difficulty, we first revisit the linear discriminative Laplacian eigenmaps (LDLE) in this paper, and further add a Bregman divergence (BD) based regularization term into it. The experiment results demonstrate that, the combination of LDLE and BD can learn a good linear transformation of polarimetric synthetic aperture radar (PolSAR) data and improve classification performances.

This paper proposes a scattering model based segmentation technique for polarimetric synthetic aperture radar (PolSAR) images. The method is composed of two main parts: merging predicate and merging order. The merging predicate is based on the idea of the fractal network evolution algorithm (FNEA). The heterogeneity of the scattering characteristics between adjacent regions is calculated to judge whether the two adjacent regions should be merged, and the merging order is determined by the gradient. Experiments using AIRSAR L-band PolSAR data confirm the good segmentation performance of the proposed method.

Accurate and timely change detection of Earth's surface features is extremely important for understanding relationships and interactions between people and natural phenomena. Post-Classification Comparison (PCC) methods based on supervised change detection are widely used in change detection for remote sensing images, but are easily affected by a significant cumulative error of single remote sensing image classification. Unsupervised change detection methods are affected by the speckle noise and cannot explicitly identify the types of land cover or land use transitions. To solve those problems, this paper proposes a change detection method based on similarity measure and joint classification. The similarity measure is obtained by test statistic and Kittler and Illingworth minimum-error thresholding algorithm (TSKI), which is used to automatically control the joint-classification classifier. The efficiency of the proposed method is demonstrated by the polarimetric synthetic aperture radar (PolSAR) images acquired by Radarsat-2 over Wuhan of China. The experimental results show that the method can identify different types of land cover changes and reduce the false alarms in the change detection.

A lodged wheat detection algorithm by applying a false alarm rate to the circular-pol correlation coefficient (CCC) and the total scattered power (Span) is proposed. The CCC is first used to identify non-lodged wheat, which is reflection symmetry. The Span feature is introduced to distinguish lodged wheat from canola in the study area, according to their large difference in scattering intensity. The Polarimertric synthetic aperture radar (PolSAR) image acquired by the Radarsat-2 satellite over the Yigen farmland of China, was used to validate the effectiveness of the proposed approach. The results indicate the potential of using only post-event PolSAR image to detect the lodged wheat.

Land-cover classification in polarimetric synthetic aperture radar images is a vital technique that has been developed for years. The Wishart distribution, which the polarimetric coherence matrix obeys, has been researched to design the well-known Wishart classifier. This model is appropriate for homogeneous scenes, but it usually fails in reality when a category consists of several subcategories or clusters. Therefore, a simple but powerful sample-merging strategy is proposed to generate representative subcenters, based on a dissimilarity measure. In addition, a weighted likelihood-ratio criterion is also proposed to further improve the performance of the Wishart distribution-based classification, based on the Wishart test statistic. Two experiments on EMISAR and UAVSAR data sets confirm that combining the proposed strategies can achieve better results than can the Wishart classifier and the other existing methods.

The mean shift algorithm shows a good performance in optical image segmentation. However, conventional mean shift algorithm performs poorly if it is used directly to synthetic aperture radar (SAR) image due to the large dynamic range and strong speckle noise. Recently, a generalized mean shift (GMS) algorithm with an adaptive variable asymmetric bandwidth was proposed for polarimetric SAR (PolSAR) image filtering. In this paper, it is further developed and extended for PolSAR image segmentation. The proposed algorithm can be used for PolSAR image superpixel segmentation directly without any preprocessing steps. Experiments using AirSAR and ESAR L-band PolSAR data demonstrate the effectiveness of the proposed superpixel segmentation algorithm.

In this paper, we investigate the separability of targets with weak backscattering on synthetic aperture radar (SAR) images by using of an unsupervised classification method. This technique is a combination of the Cloude target decomposition and the likelihood ratio test based on complex Wishart distribution for the polarimetric covariance matrix. The polarimetric SAR (PolSAR) image is initially classified by the H - α plane into eight classes. The dissimilar distance measure is derived from the statistical test of equality of covariance matrices. Significant improvement of the classification results are observed for weak backscattering targets in iterations. The effectiveness of this algorithm is demonstrated using a Radarsat-2 PolSAR image in C band and an ALOS PALSAR PolSAR image in L band.

This study investigates the characteristics of periodic surface in agriculture by the use of C-band polarimetric synthetic aperture radar (PolSAR) imagery. The scattering characteristics of the periodic potato fields in different directions are highlighted using a set of polarimetric parameters. Enhanced coherent scattering is observed when the alignment direction of ridging patterns is perpendicular to radar's line of sight (LOS). There are higher copolarized backscattering coefficients and unaffected cross-polarized backscattering coefficient for the coherent scattering. The increased copolarized correlation coefficient, reduced entropy and polarimetric alpha angle indicate that the induced coherent scattering has small scattering randomness and the odd scattering is its dominant scattering mechanism.

In this letter, a comprehensive study of the mapping of building collapse levels by the use of postearthquake synthetic aperture radar (SAR) images is addressed. Although previous studies have successfully quantified the collapse level by the use of postevent SAR, the types of features that are of benefit to the final accuracy still remain unknown. This letter takes the Yushu earthquake as a case study to contribute in two key areas. First, the Chinese dual-band airborne SAR mapping system (CASMSAR), which collected very-high-resolution X-band and P-band images (0.50 and 1.1 m, respectively) by interferometric and polarimetric modes, is comprehensively evaluated for the first time. Second, to discriminate intact and fallen structures, multiconfiguration SAR data features are analyzed, including 40 polarimetric, 3 interferometric, and 138 texture features. Furthermore, a random-forest decision framework is introduced to quantify the importance score of each feature and to improve the discrimination accuracy. The CASMSAR experiment results indicate that the texture is recommended as a powerful input to quantify the extent of building collapse in Yushu.

This paper proposes a polarimetric homogeneity measurement and applies it to the speckle filtering of polarimetric synthetic aperture radar (PolSAR) data. First, a line-and-edge (LAE) detector that can detect both the lines and edges in one scan is developed based on the traditional edge detector. A polarimetric homogeneity measurement is then derived by combining the equivalent number of looks and the LAE maps and is used to distinguish the homogeneous and heterogeneous regions. Finally, a new adaptive-window PolSAR filtering algorithm based on the LAE detector and the polarimetric homogeneity measurement is proposed. The proposed speckle filter adjusts the filtering windows in both shape and size, based on the homogeneity and gradient information. Consequently, it uses small and nonsquare windows in heterogeneous regions to preserve the detail information and uses large and square windows in homogeneous regions to maximize the suppression of speckle noise. EMISAR and ESAR L-band PolSAR data were used to demonstrate the effectiveness of the proposed filter in speckle suppression, detail preservation, and polarimetric information preservation.

The statistical region merging (SRM) algorithm exhibits efficient performance in solving significant noise corruption and does not depend on the data distribution. These advantages make SRM suitable for the segmentation of synthetic aperture radar (SAR) images, which are characterized by speckle noise and different distributions of various data types and spatial resolutions. However, the original SRM algorithm is designed for RGB and gray images characterized by additive noise and having a range of [0, 255]. In this letter, the SRM algorithm is generalized so that it can be applied to images with larger range and multiplicative noise. The original 4-neighborhood models are also generalized into 8-neighborhood models. The effectiveness of the generalized SRM (GSRM) algorithm is demonstrated by AirSAR and ESAR L-band Polarimetric SAR (PolSAR) data. Given that the input data of the GSRM algorithm can be single- or multi-dimensional, the proposed GSRM algorithm can be used for single- and multi-polarized as well as for fully polarimetric SAR data.

The mean shift algorithm, which uses a moving window and utilizes both spatial and range information contained in an image, is widely employed in digital image filtering and segmentation. However, because of the large dynamic range of synthetic aperture radar (SAR) images, applying the conventional mean shift algorithm directly to SAR image filtering will not produce meaningful results. This paper proposes an adaptive variable asymmetric bandwidth selection approach to be used in a newly derived generalized mean shift algorithm. The proposed mean shift algorithm is very versatile and can be used for SAR and polarimetric SAR (PolSAR) image filtering directly without any preprocessing steps. Monte Carlo-simulated PolSAR data are used to demonstrate the effectiveness of the proposed algorithm in speckle filtering by comparing it with other filters. Experimental Synthetic Aperture Radar (ESAR) L-band and Radarsat-2 C-band PolSAR data are used to evaluate its ability to preserve the polarimetric information of PolSAR data. The effects of initial value estimating and multilook processing on the filtered results are discussed at the end of this paper.

The sensitivity of polarimetric parameters to different residue patterns during harvest has been investigated in the paper. Several crop residue cover types called residue patterns during harvest have been selected. They include harvested wheat fields with burned and non-burned straws, the swathed oilseed rapes with different moisture, the rape swathes aligned in different directions, the non-lodging wheat and lodging wheat before harvest. A set of polarimetric parameters include backscattering coefficients, coherence coefficients, polarimetric phase differences, and variables derived from Cloude decomposition are made use of. Sensitivity analysis was conducted on one Radarsat-2 quad-pol image and one TerraSAR-X dual-pol image acquired over a farmland of China. The results indicate that polarimetric parameters have various responses to different residue patterns. The residue amounts, direction and crop harvest state are the main factors that result in the fluctuation of polarimetric scattering characteristics. In addition, it is revealed that multi-polarimetric image with short wavelength has the potential to guide the farming behaviors during harvest.

This letter introduces an efficiency-manifold-learning-based supervised graph embedding (SGE) algorithm for polarimetric synthetic aperture radar (POLSAR) image classification. We use a linear dimensionality reduction technology named SGE to obtain a low-dimensional subspace which can preserve the discriminative information from training samples. Various POLSAR decomposition features are stacked into the input feature cube in the original high-dimensional feature space. The SGE is then implemented to project the input feature into the learned subspace for subsequent classification. The suggested method is validated by the full polarimetric airborne SAR system EMISAR, in Foulum, Denmark. The experiments show that the SGE presents a favorable classification accuracy and the valid components of the multifeature cube are also distinguished.

This letter addresses a statistical model-based decomposition solution for polarimetric synthetic aperture radar imagery. The Wishart distribution is introduced to the two-component Freeman-Durden (2FD) model to enhance the traditional direct solution (2FD-DS) accuracy. This letter proposes a maximum-likelihood estimator (MLE) (2FD-MLE) expression which is simple enough to numerically solve 2FD unknowns. Furthermore, the statistical randomness impact is observed for the first time. The authors go on to verify that the decomposition results can be greatly improved by MLE, even in a simple physical model. The experiments show that the MLE enhances the estimation accuracy of land-cover types. At a moderate-look scale, the 2FD-MLE has less negative span flaws than the 2FD-DS method, and the estimation results are more close to the physical interpretation.