This paper uses the Bloch sphere formalism to introduce a quantum-inspired representation of full polarimetric Synthetic Aperture Radar (SAR) targets. By mapping SAR target vectors to qubit states in an orthonormal trihedral-dihedral basis, we demonstrate that scattering mechanisms can be effectively modeled as quantum states. Our method constructs a real 4D Stokes-like vector from the expectation values of Pauli spin operators, providing a geometrically interpretable Bloch vector. This vector precisely locates target states on the unit sphere, enabling intuitive visualization of scattering behavior for polarimetric analysis. The proposed qubit representation enhances interpretability and paves the way for quantum-computational processing of polarimetric SAR data in remote sensing applications.

Detecting rice transplantation dates is crucial for understanding its effect on grain yield and water consumption at regional scales. Traditionally, identifying the rice transplantation phase using dual-polarized (dual-pol) synthetic aperture radar (SAR) data has relied on backscatter intensity due to its characteristic low values during the flooding stage. This study leverages a recently proposed dual-pol radar surface index (DpRSI) to analyze the spatiotemporal dynamics of the rice transplantation phases. Using this index, we propose an unsupervised framework to identify rice transplantation dates. The framework is evaluated using ground-truth (GT) data over rice-cultivated regions in Vijayawada, India, during the kharif season 2018, demonstrating its effectiveness in detecting shifts in transplantation dates over a large spatial extent.

Synthetic aperture radar (SAR) data classification has gained significant research interest, as accurate land-cover information is vital in a wide range of planning and management activities. While classification algorithms for full-polarimetric (full-pol) SAR data are typically based on the statistical or physical characteristics of the scattering mechanism from targets, classification of co-cross polarization (VV-VH or HH-HV) dual-polarimetric (dual-pol) SAR data has traditionally relied on backscatter intensity information due to its limited polarimetric information. Several studies also employ the dual-pol entropy/alpha decomposition parameters, establishing a conventional framework for supervised and unsupervised classification of dual-pol SAR data. However, it is essential to note that the conventional approach cannot differentiate between certain elementary targets, leading to misclassification among diverse land-cover targets. To address this limitation, we introduce an unsupervised clustering technique for dual-pol Sentinel-1 SAR data utilizing the conventional entropy parameter alongside a dual-pol target characteristic parameter that discriminates between various land-cover targets, including “dihedral-like” (buildings, etc.) and “surface-like” (water bodies, etc.) targets in a dual-pol scene. Thus, the proposed clustering scheme, which applies to both single look complex (SLC) and ground range detected (GRD) SAR, categorizes it into eight clusters, each representing specific target characteristics. We adopted two strategies to assess the proposed clustering scheme: 1) cluster zones obtained for diverse land-cover targets spanning continents and 2) temporal changes in cluster zones over rice-cultivated fields at various growth stages. The proposed approach effectively discriminates diverse land-cover targets and distinct growth stages of rice.

This paper explores four distinct target descriptors derived from full-polarimetric Synthetic Aperture Radar (SAR) data. Initially, we define a 2 × 1 real positive target vector by leveraging the mean and standard deviation of complex eigenvalues extracted from the 2 × 2 Sinclair matrix. This vector is the basis for two innovative parameters: 1) the scattering-type parameter, and 2) the scattering asymmetry parameter. Furthermore, we introduce the scattering purity and complexity parameters derived from the mean and standard deviation of the real positive eigenvalues of a Hermitian positive semi-definite 3 × 3 coherency (covariance) matrix. We highlight the efficacy of these parameters by conducting an experimental analysis with several canonical targets. Subsequently, we investigate their performance by thoroughly examining Radarsat-2 full-polarimetric SAR data.

This paper proposes a novel multivariate Gaussian Process Regression (GPR) approach for multi-class crop classification. We have trained and validated the proposed model utilising backscatter information from E-SAR C- and L-band dual-polarimetric data acquired during the AGRISAR 2006 campaign. Further, we use the Product of Experts (PoE) fusion strategy to combine decisions from the proposed Gaussian Process (GP) models trained and validated independently over C- and L-band data to analyze the changes in the classification performance. The synergistic C- and L- band information show an improved classification accuracy during various phenological stages of major crop types by (a) 4 to 37 % for VV-VH backscatter intensity channels and (b) 1 to 39 % for HH-HV backscatter intensity channels.

Given the exceptional effectiveness of deep Convolutional Neural Networks (CNNs) in computer vision, there has been a recent surge of interest in employing CNNs for various applications in image classification. Additionally, scientists are exploring the potential of vision transformers for Earth observation applications, owing to their recent tremendous success. However, a major challenge with vision transformers is their increased demand for training data compared to CNN classifiers. Furthermore, vision transformers exhibit quadratic complexity and necessitate substantial hardware resources. In the context of PolSAR image classification, we propose the PolSARConvMixer—a fundamental framework that segregates the mixing of spatial and channel dimensions, maintains uniform size and resolution across the network and directly processes PolSAR image patches as input. Our experiments on two PolSAR data benchmarks, namely Flevoland and San Francisco, demonstrate the significant superiority of the developed PolSARConvMixer over several other algorithms, including AlexNet, ResNet, FNet, a 2D CNN, and PolSARFormer.

Crop residue burning (CRB) is a common post-harvest practice in India, where approx. 150 metric tonnes of crop residue are burned annually. CRB has adverse consequences: air quality degradation with pollutants affecting respiratory health and soil structure. Remote sensing data are employed to spatially assess air quality degradation, providing a comprehensive understanding of CRB’s spatial and temporal dynamics and environmental implications. This study introduces a new multi-band vegetation index using Sentinel-2 satellite imagery to map crop residue burning. We then assess its impact on air quality with parameters like aerosol, particulate matter (PM2.5), carbon monoxide, and nitrogen oxides from satellites and ground stations.

This paper utilizes the algebraic property of the Gershgorin circle theorem to explore the association between the orthogonal odd- and even-bounce scattering mechanisms. First, we use two unitary transformations to decouple this association, thereby reducing the ambiguity in the underlying scattering mechanisms. We then derive a descriptor utilizing the properties of circles in the Gershgorin theorem to gauge the mixture property of coherent type scattering in a pixel. We explain and validate the proposed findings with suitable experiments and examples from the full polarimetric L-band PiSAR Tomakomai dataset. The results demonstrate the effectiveness of the proposed approach in characterizing deterministic scattering mixtures.

Synthetic Aperture Radar (SAR) data, particularly for Asian countries, are valuable in monitoring crops. Scattering information extracted from full-polarimetric (FP) SAR data offers exceptional sensitivity to crop water content and geometrical properties. Thus, they are widely used for continuous crop monitoring throughout their growth stages. However, many of these methods are limited to FP SAR data. This study introduces a new approach to obtain scattering power components from SLC and GRD dual-polarimetric (DP) SAR data. We found the scattering powers obtained from the dual-pol SAR data to be sensitive to changes in the crop morphology as it progresses to advanced growth stages.

Characterizing targets with dual-polarimetric (dual-pol) Synthetic Aperture Radar (SAR) data has traditionally relied only on backscatter intensity. However, the limitations of conventional dual-pol parameters, such as the inability to differentiate between orthogonal targets like dihedral and trihedral structures, result in misclassification among diverse targets. This study proposes an innovative target characteristic parameter derived from both dual-pol single-look complex (SLC) and ground range detected (GRD) SAR data that effectively distinguishes between "dihedral-like" and "surface-like" targets, enabling improved characterization of diverse land cover targets.

This study proposes a target characterization technique using a complex scattering trace correlation measure. Several orthonormal projections for characterizing scattering information have been presented in the literature. For instance, one can project a particular target on different polarization bases. In doing so, two possible phenomena can happen: 1) either the structure of the target scattering vector entirely changes from one basis to another or 2) the vector retains some of its inherent characteristics while some get altered. Therefore, the complex correlation between the two projections provides information about the typology of the target present in the scene. Hence, in this study, we derive the amplitude and phase of the complex correlation measure between the linear and circular bases to describe different land cover targets. We have analyzed the proposed technique using the GaoFen-3 single look complex (SLC) image over San Francisco Bay, USA, the RADARSAT-2 SLC image over Vijayawada, India, and the ALOS PALSAR image over the coast of Futtsu, Japan. The results of these tests exhibit significant potential for retrieving comprehensive physical characteristics of the targets within the observed scenes.

Characterization and classification of natural and human-made targets using polarimetric Synthetic Aperture Radar (SAR) data have been widely explored for diverse applications. The Cloude and Pottier scattering-type parameter α has become a standard tool for target characterization and classification. However, it fails to discriminate between all canonical targets. In response to this limitation, this research introduces an alternative scattering-type parameter capable of distinguishing all canonical targets. To achieve this, we first define a real 2 × 1 vector comprising two roll-invariant descriptors derived from the 2 × 2 complex scattering matrix. Subsequently, we calculate the Euclidean norm of this vector relative to a reference real vector associated with a standard dipole scatterer. We leverage this computed Euclidean norm as a key element in formulating our alternate scattering-type parameter. The alternate scattering-type parameter is formulated to effectively characterize a wide spectrum of targets, encompassing both coherent and incoherent. We systematically evaluate the performance of our proposed alternate scattering-type parameter against the well-established Cloude-Pottier α parameter for a diverse set of targets. Additionally, we introduce a target classification framework for dominant scatterers utilizing the vector with two roll-invariant descriptors. To validate our approach, we conducted experiments utilizing two full-polarimetric Earth observation datasets acquired in the C- and L- bands and one full-polarimetric Lunar dataset in the L-band. These datasets were selected to showcase and validate the efficacy of both the alternate scattering-type parameter and the target classification framework.

Effective monitoring of wetlands plays a pivotal role in comprehending and managing these ecologically vital ecosystems. This study assesses the potential of C-band synthetic aperture radar (SAR) imagery in compact polarization (CP) mode, utilizing the RADARSAT Constellation Mission (RCM), for wetland characterization. We introduce the compact-polarimetric signature (CPS) as a novel descriptor to delineate wetlands, including bog, fen, and marsh classes. In addition, we propose an alternative decomposition technique ($\mu -\chi $) to segment the total power into three components: odd-bounce scattering$(P_{s})$, double-bounce scattering$(P_{d})$, and random scattering$(P_{v})$. For our evaluation, we selected a test site in New Brunswick, Canada, and acquired a series of RCM datasets covering this region. The time-series CPS plots yield valuable insights, elucidating the scattering mechanisms of different wetland classes. Notably, these plots reveal that during the active season, characterized by changing vegetation structures, the scattered waves exhibit variations, leading to changes in received power and the purity parameter ($\mu $). Furthermore, the observed variations in the proposed power components demonstrate a significant discriminatory capacity among wetlands. The$P_{s}$,$P_{d}$, and$P_{v}$components effectively distinguish bog, fen, and marsh classes, respectively, capturing the unique characteristics of each wetland type. These findings carry considerable potential for advancing wetland characterization through the RCM CP-SAR mission. The improved discriminative ability among different wetland classes is a valuable contribution to the broader field of wetland ecology and management. This advancement potentially empowers precise wetland classification, facilitating well-informed decision-making in wetland preservation and resource allocation. The applications of these findings extend to ecosystem monitoring, environmental impact assessments, and the long-term evaluation of wetland health. Eventually, this contributes to developing more effective wetland conservation and management strategies.

Change detection is a fundamental task that involves assessing changes in a given region over multiple time periods. It has been widely applied across various fields, including monitoring deforestation, urban expansion, and natural disaster analysis. In this article, we address the critical and complex issue of automatically identifying types of changes in land cover using remotely sensed imagery. While conventional unsupervised change detection methods typically focus on comparing pairs of images and making a binary decision between “change” and “nonchange,” our approach tackles the challenge of analyzing long image series and identifying the kind of change. Under this condition, the unsupervised change detection process allows for a more informative identification of the land cover dynamics. Moreover, our approach transforms input data to a new representation, capturing the target’s spectral response changes over time. Through the utilization of stochastic distances and an optimized thresholding scheme, areas exhibiting minimal spectral response variance are classified as unchanged, effectively distinguishing them from regions undergoing modifications. Next, by applying autocorrelation analysis, regions exhibiting temporal modifications are segregated into periodic (i.e., seasonal) and aperiodic (i.e., permanent) change cases. Experimental validation using both simulated and real-world remote sensing image series demonstrates the effectiveness of the proposed approach.

Target characterization parameters are pivotal in accurately identifying and assessing diverse land cover targets in radar polarimetry. While full-polarimetric (full-pol) synthetic aperture radar (SAR) data offer numerous parameters, characterizing targets with HH-HV or VV-VH dual-polarimetric (dual-pol) SAR data has traditionally relied on backscatter intensity alone due to limited polarimetric information, which leads to ambiguities in characterizing diverse land cover targets. In response to this limitation, this study introduces a novel target characteristic parameter$\overline {\alpha }_{(k)}$derived from dual-pol single-look complex (SLC) and ground range detected (GRD) SAR data that are capable of discriminating between “dihedral-like” (buildings, bridges, ships, and so on) and “surface-like” (water bodies, bare fields, runways, and so on) targets, by employing a data-driven approach. We first derive a set of normalized descriptors independently of SLC and GRD SAR data to formulate two indices that characterize “dihedral-like” and “surface-like” targets. Using the two indices, we derive the dual-pol target characteristic parameter, providing a novel perspective on the intricate nature of radar responses from diverse land cover targets acquired by dual-pol SAR sensors. Furthermore, we employ this parameter to extract three scattering power components: “dihedral-like” ($P_{d-l}$), unpolarized ($P_{u}$), and “surface-like” ($P_{s-l}$) from both dual-pol SLC and GRD SAR data. We assess the proposed target characteristic parameter and scattering power components using Sentinel-1 images acquired over diverse land cover targets spanning six continents. This novel approach enables improved global land cover characterization with operational SAR missions such as Sentinel-1 and upcoming NASA-ISRO SAR (NISAR) missions.

Driven by the demand for enhanced performance and efficiency of power electronic converters, this article presents a novel architecture for the high-definition sinusoidal pulseidth modulation technique. The proposed architecture combines the two dc-linked capacitor voltages and three reference voltage signals of a three-level neutral-point clamped (NPC) inverter to effectively address the imbalances in dc-linked capacitor voltages, even during the transient conditions. By adding an offset voltage to the three-phase reference voltage, the proposed strategy achieves robust voltage balancing. A mathematically formulated voltage balancing algorithm has been developed to calculate the injected zero-sequence voltage and compensating neutral-point voltage, enabling versatile operation across a wide range of power factor angle and modulation index. The proposed approach offers several advantages, including high-resolution and high-frequency performance, reduced switching losses, higher efficiency, and minimal field-programmable gate array resource utilization. Notably, it eliminates the need for closed-loop controllers and three-phase current information, resulting in a favorable balance between functionality and design complexity. The exceptional capabilities and potential of the proposed strategy are showcased through detailed design considerations, theoretical analysis, and experimental validation on a small-scale NPC inverter prototype. Furthermore, to highlight its efficacy, a comprehensive comparative analysis has been conducted, evaluating the proposed architecture against recently-reported similar techniques.

Antigen-presenting cells present peptides derived from foreign pathogens in the form of peptide-MHCII complexes to induce immune response. Two important and interrelated processes that lead to peptide presentation are antigen processing and peptide-MHCII binding. Although the latter phenomenon has been thoroughly studied and incorporated into CD4+ T cell epitope prediction tools, antigen processing has remained under-explored as a means to improve epitope prediction tools. In prior work, our group developed the antigen processing likelihood (APL) metric [1] and showed that it can be used to effectively predict CD4+ T-cell epitopes either alone or in combination with peptide-MHCII binding affinity-based tools. A key component of the APL scoring approach is a residue-wise conformational stability metric known as COREX [2], which estimates the likelihood of unfolding at each residue using a free energy approximation. COREX calculation takes a significant amount of time (6-8 hours) and thus limits our ability to rapidly predict CD4+ T-cell epitopes over large antigen sets. In this paper, we give a parallel algorithm to compute the COREX metric and demonstrate its utility over several benchmarks. We achieve a 196-fold speedup with our parallel algorithm, bringing the overall computation of APL down from hours to minutes.

In this paper, a novel fourth-order voltage controller has been designed for an isolated Dual Active Bridge DC-DC step-up converter for battery discharge applications. State space small signal average modeling of Dual Active Bridge DC-DC converter differs from the conventional DC-DC converters since the leakage inductance of Dual Active Bridge converter is in series with the high-frequency isolation transformer, and hence has a pure alternating ripple current, instead of the large average small ripple current for the traditional DC-DC converters. Generalized Average Modelling using a first harmonic approximation of the state variables for the converter with a high-frequency transformer and a step-up turns ratio ‘N’ is presented. Furthermore, actual frequency response plot of the controller transfer function and its application to actual Dual Active Bridge Converter circuit is simulated in MATLAB Simulink and presented in this paper. The optimal design of the Dual Active Bridge Converter with Single Phase Shift modulation is also discussed in detail for the given specifications. This paper also discusses the suitability of the system for a smooth transition from the constant voltage control of the designed Dual Active Bridge voltage controller to a voltage control of an added Solar Photovoltaic based Boost converter for a Multi-input architecture of Solar Photovoltaic and battery-fed application for the same load with the designed controller.

Several orthonormal projections onto various bases have been proposed to analyze Polarimetric Synthetic Aperture Radar (PolSAR) data. These individual projections frequently lead to several additional ambiguities for target scattering characterization. Therefore, considerable confusion is common when using unsupervised classification approaches to classify targets. In this study, we project the scattering information onto several realizations of the normalized scattering configuration without imposing an orthogonality requirement. Using the full-polarimetric AIRSAR data over San Francisco, USA, we first compute the spectrum of the scattering-type parameter, θFP, and subsequently use it to categorize various land-cover targets.

This study contributes to advancing the understanding and utilization of compact polarimetry (CP) RCM Synthetic Aperture Radar (SAR) data for enhanced crop characterization and mapping. The received wave polarization signature captures the explicit variation of the received power with a fixed transmit polarization and varying received polarization bases. This information is then suitably utilized for improved discrimination among multiple crop types. Furthermore, the study explores using multi-date polarimetric features extracted from RCM imagery to achieve more accurate and detailed crop mapping results. By incorporating information from multiple acquisition dates, the multi-date polarimetric features illustrate excellent potential in capturing temporal variations in crop characteristics, leading to enhanced crop mapping accuracy. The implications and findings from this study could be essential in demonstrating the role of RCM data in agricultural applications.

In this paper, we propose a new hybrid methodology to decompose the polarimetric synthetic aperture radar (PolSAR) coherency matrix into sum of three basic scattering mechanisms. The proposed methodology first utilizes a physical scattering model to compute the volume scattering contribution using generalized eigen-decomposition approach. Later, the surface and dihedral scattering powers are computed simultaneously utilizing eigenvalues and dominant eigenvector (α1) of the remainder coherency matrix. We further enhance the performance of the proposed approach by two special unitary transformations which optimize α1parameter by increasing its dominancy towards a single scattering-type phenomenon. We demonstrated the superiority of the proposed two approaches by comparing the experimetal results on a fully polarimetric SAR dataset with recent state-of-the-art techniques.

The study of soil moisture is crucial for understanding the hydrological cycle and its impact on energy and water exchanges at the land-atmosphere interface. Synthetic Aperture Radar (SAR) data, such as Sentinel-1, has shown potential for estimating soil moisture at high spatio-temporal resolutions. However, the sensitivity of SAR responses to soil moisture and the applicability of Sentinel-1 data for soil moisture estimation at different land covers require further investigation. In this paper, we evaluate the sensitivity of Sentinel-1 data for soil moisture estimation and compare the estimated soil moisture for different land covers. A change detection approach combined with a vegetation scattering model is employed to estimate soil moisture. The results demonstrate that while the change detection approach with vegetation correction improves soil moisture estimation, the accuracy is still not within an acceptable range for plot-level decision-making, such as irrigation management. However, the results show that the soil moisture information obtained from Sentinel-1 data can be suitable for regional-level monitoring applications and decision-making.

Aquacultural rafts are important for a large-scale utilisation and conservation of marine resources. Synthetic Aperture Radar (SAR) sensors are viable for mapping and monitoring aquacultural structures. This research offers a novel method that uses dual polarimetric Sentinel-1 and Sentinel-2 data. We project the covariance matrix, obtained from Sentinel-1 onto random normalised scattering configurations. Following this we obtain a spectrum of the dual-polarimetric scattering-type parameter θDP. This θDPspectrum is used with the Random Forest Classifier to classify the rafts from water surface. These classified maps are compared with the conventional eigen decomposition based approach. It is found that the classification accuracy of the proposed technique outperforms the accuracy obtained from the eigen-based technique.

Convolutional neural networks (CNNs) have recently found great attention in image classification since deep CNNs have exhibited excellent performance in computer vision. Owing to their immense success, of late, scientists are exploring the functionality of transformers in Earth observation applications. Nevertheless, the primary issue with transformers is that they demand significantly more training data than CNN classifiers. Thus, the use of these transformers in remote sensing is considered challenging, notably in utilizing polarimetric synthetic aperture radar (PolSAR) data, due to the insufficient number of existing labeled data. In this letter, we develop and propose a vision transformer (ViT)-based framework that utilizes 3-D and 2-D CNNs as feature extractors and, in addition, local window attention (LWA) for the effective classification of PolSAR data. Extensive experimental results demonstrated that the developed modelPolSARFormerobtained better classification accuracy than the state-of-the-art vision Swin Transformer and FNet algorithms. ThePolSARFormeroutperformed the Swin Transformer and FNet by the margin of 5.86% and 17.63%, in terms of average accuracy (AA) in the San Francisco data benchmark. Moreover, the results over the Flevoland dataset illustrated that thePolSARFormerexceeds several other algorithms, including the ResNet (97.49%), Swin Transformer (96.54%), FNet (95.28%), 2-D CNN (94.57%), and AlexNet (91.83%), with a kappa index (KI) of 99.30%. The code will be made available publicly athttps://github.com/aj1365/PolSARFormer.

This work proposes a methodology that begins by extracting a rank-deficient residue matrix by suitably subtracting a volume scattering model from the measured full-rank coherency matrix. Then, two unitary rotation matrices transform the residue matrix aiming to decorrelate single and double-bounce scattering mechanisms. The rotated residue matrix is eigen-decomposed as the sum of two rank-1 matrices. A normalized target symmetry-asymmetry difference index is proposed that is computed from the dominant rank-1 coherency matrix elements. This index relates the two Huynen parameters: the generator of target symmetry (A0) and the generator of target structure (B0). The dominant scattering power components are computed using this proposed normalized difference index. The performance of the proposed approach is evaluated using two polarimetric Synthetic Aperture Radar (PolSAR) datasets. Analysis shows that the obtained results outperform the state-of-the-art techniques.

This letter presents a new decomposition approach with independent physical scattering models for odd- and even-bounce mechanisms. The rationale behind the method is to provide orthogonality between the odd- and even-bounce scattering components by removing their correlation from a rank-deficient residue coherency matrix by two successive unitary rotations. The rotated residue coherency matrix is then decomposed into the sum of two rank-1 orthogonal Hermitian positive semi-definite matrices. We show that these two orthogonal matrices consistently depict a single-type scattering phenomenon. Therefore, without branching criteria, one can independently compute the nonnegative odd- and even-bounce scattering powers. We used two full polarimetric SAR data to validate the effectiveness of the proposed method. We first affirm the orthogonality of the proposed scattering models. Then, we perform decomposition to derive the scattering power components and compare them with conventional and state-of-the-art methods. The quantitative analysis supports the merit of the proposed method.

Classification of very high-resolution (VHR) aerial remote sensing (RS) images is a well-established research area in the RS community as it provides valuable spatial information for decision-making. Existing works on VHR aerial RS image classification produce an excellent classification performance; nevertheless, they have a limited capability to well-represent VHR RS images having complex and small objects, thereby leading to performance instability. As such, we propose a novel plug-and-play multiscale attention feature extraction block (MSAFEB) based on multiscale (MS) convolution at two levels with skip connection, producing discriminative/salient information at a deeper/finer level. The experimental study on two benchmark VHR aerial RS image datasets (AID and NWPU) demonstrates that our proposal achieves a stable/consistent performance (minimum standard deviation (SD) of 0.002) and competent overall classification performance (AID: 95.85% and NWPU: 94.09%).

In this work, we propose two parameters in radar polarimetry: (i) scattering purity and (ii) scattering complexity. To obtain these expressions, we use inequalities on the bounds of the condition number in terms of the mean ( $m$ ) and standard deviation ( $s$ ) of the eigenvalues of a Hermitian positive semi-definite matrix. The polarimetric scattering purity characterizes the overall polarization structure in the scattered wave. In contrast, the polarimetric scattering complexity describes the mixture of orthogonal polarized pure components in the scattered wave. We discuss the variability of these metrics over various land cover classes using full-polarimetric C-band Synthetic Aperture Radar (SAR) data. We compare their spatial variations over the ocean surface, built-up areas, and vegetation. We notice significant differences in the purity and complexity characteristics across a wide range of targets in the scene.

Polarimetric Synthetic Aperture Radar (SAR) data has been extensively used to estimate soil permittivity because of its high sensitivity to the dielectric properties of the target. However, the presence of vegetation cover induces bias in the permittivity estimates. This work utilizes the scattering-type parameters: alpha$(\overline{\alpha})$and theta$(\theta_{\text{FP}})$to estimate soil permittivity using the X-Bragg as the dominant surface scattering model. A theoretical study ascertains that the recently proposed$\theta_{\text{FP}}$is fairly robust towards the depolarizing component in the X-Bragg model. Hence, it is expected to produce better inversion accuracy. This study analyzes major phenology stages of Canola using the UAVSAR full-pol SAR data and the ground measurements acquired during the SMAPVEX12 campaign over Manitoba, Canada. The proposed method achieved an RMSE of 5.9 for soil permittivity with a Pearson coefficient,$r=0.83$. Further, the temporal trend of the soil permittivity estimates also agrees with in-situ measurements for the entire timeframe.

We propose an unsupervised methodology for multi-class change detection (CD) in multimodal remote sensing data fused using the Kronecker product formalism. The method utilizes the compressed change vector analysis (C2VA) on the fully vectorized change matrices. The multimodal case is demonstrated using dual-frequency full-polarimetric Syn-thetic Aperture Radar (SAR) data obtained by EMISAR over the Foulum agricultural area. The change types are inves-tigated using ground truth data for the growth of various crops. The work showcases the capability of the Kronecker product-based CD formalism beyond conventional scalar change indices.

In continuance to quad-pol SAR data analysis, Cloude proposed the eigenvalue-eigenvector decomposition of dual-pol 2 x 2 covariance matrix to compute the target characterization parameter, ᾱ. In this study, we explicitly demonstrate an alternative approach to compute the dual-pol ᾱ using De-schamps parameters defined using a spherical triangle structure on the Poincaré sphere. The geometrical construction provides inherent visual information in terms of the Stokes vectors for diverse targets characterized by HH-HV and VV-VH data. This technique produces the same result as proposed by Cloude. We utilized canonical targets and airborne SAR data to demonstrate the results with the proposed approach.

Built-up area (BA) mapping is vital for understanding the effect of the urban regions on the environment, thereby supporting sustainable development. This study proposes a new dual-pol radar built-up area index (DpRBI) to detect the BA using Sentinel-1 SAR data. The DpRBI formulation is based on the three Stokes vector elements of the scattered wave derived from the 2 × 2 covariance matrix C2. This study uses the Sentinel-1 SAR data sets over Milan, Italy and Barcelona, Spain to map the BA using DpRBI. The overall accuracy of the BA extracted using the proposed technique was found to be 84.19% and 87.95% over Milan and Barcelona, respectively. It is noteworthy that even relatively small low-density BA is precisely classified using the proposed built-up index.

Domain-agnostic data retrieval has lately become essential amidst the availability of large-scale data from different types of sensors. However, the unavailability of a sufficient amount of samples of certain classes during training curtails the utility of existing retrieval models in remote sensing (RS) applications. Here, we propose a novel framework for zero-shot intermodal data retrieval of RS data. Thereupon, we design an encoder–decoder structure that ensures enhanced overlapping among the two data domains utilizing cross-triplet and cross-projection loss functions. Furthermore, we propose a sketch-based representation of the RS databaseEarth on Canvaswith diverse classes. We perform a thorough benchmarking of this data set and demonstrate that the proposed framework outperforms state-of-the-art methods for zero-shot sketch-based retrieval framework for RS data.

Graph convolution networks (GCNs) are useful in remote sensing (RS) image retrieval. It is found to be effective because, in a graph representation, the relative geometrical interactions between different regions (or segments) are appropriately captured, along with their region-wise features in their region adjacency graphs. Also, the attention mechanism has often been applied to the nodes to highlight the essential features in each node. In this regard, a significant amount of high-frequency information is missed since each image segment is effectively summarized within a single node. To account for this and increase the learning capacity, we propose to attend over the edge/adjacency matrix to highlight the interactions among meaningful regions that contribute to supervised learning from images. We exploit this novel edge attention mechanism together with node attention to highlight essential image context by allowing more importance to the meaningful neighboring regions that highlight a relevant node. We implement the proposed context-attended GCN framework for image retrieval on the benchmarked UC-Merced and the PatternNet datasets. We observe a notable improvement in the results compared to the state of the art.

Building footprints and road network detection have gained significant attention for map preparation, humanitarian aid dissemination, disaster management, to name a few. Traditionally, morphological filters excel at extracting shape features from remotely sensed images and have been widely used in the literature. However, the structural element (SE) dimension selection impedes these classical and learning-based methods utilizing any morphological operators. To overcome this aspect, we propose a novel framework to extract road and building from remote sensing (RS) images by exploiting morphological networks. The method predominantly aims at learning an optimized SE to capture variably-sized building and road footprints. We substitute convolutions with 2-D morphological operations in the basic building blocks of the network architecture (Dual-path Morph-UNet) to manage the intricate task of optimizing the SE in addition to the actual segmentation task. The dual-path framework incorporates parallel residual and dense paths in an encoder-decoder architecture, which permits learning of higher-level feature representations with fewer parameters. Finally, we implement the proposed framework on the benchmarked Massachusetts roads and buildings dataset and demonstrate superior results than the state-of-the-art (SOTA). In addition, the proposed network consists of$10\times $less learnable parameters than the SOTA methods.

This study proposes the spectrum analysis of the scattering-type parameter, θFP, to characterize different land cover targets. Many orthonormal projections of the scattering information onto distinct polarization bases have been proposed in the literature. However, these conventional orthonormal basis projection techniques often provide various target characterization ambiguities and classification. In this work, we propose projecting the target coherency matrix onto several random realizations of the normalized scattering configuration without restriction to the orthogonality constraint. This unique approach helps enhanced target characterization from polarimetric SAR data. We show the efficacy of our proposed technique over different land cover types using the C-band RADARSAT-2 data over SF and the L-band ALOS-2 data over Mumbai.

We propose a hybrid algorithm for despeckling the Synthetic Aperture Radar (SAR) images using the Convolutional Neural Network (CNN) denoising and complex wavelet shrinkage. In particular, we perform the speckle reduction process in the complex wavelet domain. We first despeckled the approximation complex wavelet coefficients using the MUltichannel LOgarithm with the Gaussian denoising algorithm (MuLoG) based on a pre-trained CNN model named FFDNet. Next, we despeckled the log-transformed details of the complex wavelet coefficients using the averaged version of the Maximum a Posteriori (AMAP) estimator. The experimental results on simulated and real SAR images showed that the proposed method achieved better speckle suppression in the homogeneous areas while preserving edges and point targets than other state-of-the-art methods.

Shallow convolutional neural networks (CNNs) have successfully been used to classify polarimetric synthetic aperture radar (PolSAR) imagery. However, one drawback of the existing deep CNN-based techniques is that the input PolSAR training data are often insufficient due to their need for a significant number of training data compared to shallow CNN models utilized in PolSAR image classification. In this paper, we propose using Haar wavelet transform in deep CNNs for effective feature extraction to improve the classification accuracy of PolSAR imagery. Based on the results, the proposed deep CNN model obtained better average accuracy in the San Francisco region with an accuracy of 93.3% and produced more homogeneous classification maps with less noise compared to the two much shallower CNN models of AlexNet (87.8%) and a 2D CNN network (91%). The proposed algorithm is efficient and may be applied over large areas to support regional wetland mapping and monitoring activities using PolSAR imagery. The codes are available at (https://github.com/aj1365/DeepCNN_Polsar).

The generalized degree of polarimetric purity is a vital descriptor widely studied and interpreted for electromagnetic wave characterization. It is invariant under the rotation of the reference frame. In this work, we first propose an alternate expression of this purity measure using the mean$(m)$and standard deviation$(s)$of the real positive eigenvalues of a Hermitian positive semidefinite matrix. We then use this expression to propose a polarimetric scattering purity and scattering complexity measure. To obtain these expressions, we use certain inequalities on the bounds of the condition number for Hermitian positive definite matrices defined in terms of$m$and$s$. The polarimetric scattering purity parameter characterizes the overall polarization structure in the scattered wave. In contrast, the polarimetric scattering complexity parameter describes the mixture of orthogonal polarized pure components in the scattered wave. First, we demonstrate the two proposed measures by analyzing two cases: 1) multiple scattering and 2) a mixture of canonical targets. Then, we utilize full-polarimetric C- and L-band synthetic aperture radar (SAR) data to describe the variation of these measures over various land cover classes. We compare their spatial variations over the ocean surface, built-up areas, and vegetation. We observe notable contrasts in the purity and the complexity parameters over a diverse mixture of targets in the scene. Finally, we critically interpret the variation of the two measures over the temporal scene of rice crop acquired by C-band full-polarimetric SAR data. These analyses affirm the importance of these measures for explicit target characterization. The open-source version of the code is available athttps://github.com/Subho07/scattering-purity-and-complexity

Soil permittivity estimation using Polarimetric Synthetic Aperture Radar (PolSAR) data has been an extensively researched area. Nonetheless, it provides ample scope for further improvements. The vegetation cover over the soil surface leads to a complex interaction of the incident polarized wave with the canopy and subsequently with the underlying soil surface. This paper introduces a novel methodology to estimate soil permittivity over croplands with vegetation cover using the full and compact polarimetric modes. The proposed method utilizes the full and compact polarimetric scattering-type parameters, θFPand θCP, respectively. These scattering type parameters are a function of the soil permittivity and the Barakat degree of polarization. The method considers the X-Bragg scattering model for the soil surface. In particular, these scattering-type parameters explicitly account for the depolarizing structure of the scattered wave while characterizing targets. Thus, the depolarization information in terms of surface roughness in the X-Bragg model gets inherent importance while using θFPand θCP, unlike existing scattering-type parameters. Therefore, the proposed technique enhances the expected value of the inversion accuracies. This study validated the major phenology stages of four crops using the UAVSAR full-pol and simulated compact pol SAR data and the ground truth data collected during the SMAPVEX12 campaign over Manitoba, Canada. The proposed method estimated permittivity with an RMSE of 2.2 to 4.69 for FP and 3.28 to 5.45 for CP SAR data along with a Pearson coefficient,r≥ 0.62.

The performance of a deep-learning-based model primarily relies on the diversity and size of the training dataset. However, obtaining such a large amount of labeled data for practical remote sensing applications is expensive and labor-intensive. Training protocols have been previously proposed for few-shot learning (FSL) and zero-shot learning (ZSL). However, FSL is not compatible with handling unobserved class data at the inference phase, while ZSL requires many training samples of the seen classes. In this work, we propose a novel training protocol for image retrieval and name it aslabel-deficit zero-shot learning(LDZSL). We use this novel LDZSL training protocol for the challenging task of cross-sensor data retrieval in remote sensing. This protocol uses very few labeled data samples of the seen classes during training and interprets unobserved class data samples at the inference phase. This strategy is critical as some data modalities are hard to annotate without domain experts. This work proposes a novel bi-level Siamese network to perform the LDZSL cross-sensor retrieval of multispectral and SAR images. We utilize the available geo-referenced SAR and multispectral data to domain align the embedding features of the two modalities. We experimentally demonstrate the proposed model’s efficacy using the So2Sat dataset compared to the existing state-of-the-art models of the ZSL framework trained under a reduced training set. We also show the generalizability of the proposed model using a sketch-based image retrieval task. Experimental results on the Earth on Canvas dataset exhibit comparative performance over the literature.

Rapid advancements have been made in model-based decomposition techniques for polarimetric Synthetic Aperture Radar (PolSAR) data. Improvements have been primarily driven by including additional scattering models to the three-component model-based method first introduced by Freeman and Durden. Nevertheless, the three-component method is still extensively used due to its simplicity and ease of interpretability. Recently, the paradigm of the decomposition strategy has been changed to non-model types with notable success. Thus utilizing this new approach, we propose a hybrid (i.e., combining non-model and model-based) three-component methodology in this work. The proposed method primarily involves three steps: (i) the generalized eigendecomposition technique is first used to determine the optimum volume scattering power, (ii) the residual rank-2 coherency matrix (i.e., volume scattering model deducted) is appropriately transformed using two unitary transformations to decorrelate the odd and even bounce scattering components, and (iii) compute the odd and even bounce scattering power contributions using the newly developed scattering-type parameter obtained from the rank-2 matrix. Each step carries relevant physical significance that is appropriately addressed in this work. The proposed methodology is first demonstrated using some specific coherency matrices from canonical targets and a few matrices extracted from different landcover types from full-polarimetric SAR images. We then apply the proposed method over diverse landcover types using two full-polarimetric SAR images. We compare the results with the state-of-the-art three-component model-based decomposition techniques to validate the effectiveness of the proposed method that deals with the existing challenges of model-based decomposition methods.

Accurate and high-resolution spatio-temporal information on wheat growth is an essential factor for agronomic management and grain yield estimation. In this study, we propose a new vegetation descriptor from the Sentinel-1 Synthetic Aperture Radar (SAR) GRD data for monitoring the growth stages of wheat. We also assess the performance of the proposed vegetation descriptor for estimating wheat biophysical parameters: Plant Area Index (PAI), Dry Biomass (DB), and Vegetation Water Content (VWC) over the Soil Moisture Active Passive Validation Experiment 2016 (SMAPVEX16-MB) test site in Manitoba, Canada. The proposed vegetation descriptor produced good correlation$(R^{2})$with the biophysical parameters of wheat: 0.63 (PAI), 0.64 (DB), and 0.57 (VWC) compared to$\sigma_{\text{VH}}^{\mathrm{o}}/\sigma_{\text{VV}}^{\mathrm{o}}$and the dual-pol Radar Vegetation Index (RVI).

In this work, we address a cross-modal retrieval problem in remote sensing (RS) data. A cross-modal retrieval problem is more challenging than the conventional uni-modal data retrieval frameworks as it requires learning of two completely different data representations to map onto a shared feature space. For this purpose, we chose a photo-sketch RS database. We exploit the data modality comprising more spatial information (sketch) to extract the other modality features (photo) with cross-attention networks. This sketch-attended photo features are more robust and yield better retrieval results. We validate our proposal by performing experiments on the benchmarked Earth on Canvas dataset. We show a boost in the overall performance in comparison to the existing literature. Besides, we also display the Grad-CAM visualizations of the trained model's weights to highlight the framework's efficacy.

Built-up area extraction from remote sensing images is essential for urban planning, disaster management and industrial development. In this study, we propose two built-up area indices for full (FP) and dual (DP) polarimetric Synthetic Aperture Radar (SAR) data. The built-up area index for FP SAR data is based on the dominant scattering mechanism of the electromagnetic (EM) waves from urban targets. In contrast, the built-up area index for DP SAR data is based on the scattering reflection symmetry property. The two proposed indexes are validated with full and extracted dual pol (VV-VH) scenes of a C-band RADARSAT-2 SAR data over urban San-Francisco. They show encouraging results in detecting urban areas within a SAR resolution cell. The overall accuracy of delineating built-up area is 84.2% for FP SAR data and 79% for DP SAR data.

Target decomposition methods for polarimetric Synthetic Aperture Radar (PolSAR) data aim at explaining the scattering information. In this regard, several conventional model-based methods use scattering power components to analyze polarimetric SAR data. However, the typical hierarchical process to enumerate power components uses various branching conditions, leading to several limitations. This study uses the 3D Barakat degree of polarization (DoP) to obtain the scattered wave polarization state. We employ the DoP to obtain the even bounce, odd-bounce, and diffuse scattering power components. Besides, we propose a measure of target scattering asymmetry, which is subsequently utilized to obtain the helicity power. All the power components in our approach are roll-invariant and non-negative, and the decomposition preserves the total power. We utilized C-band full polarimetric RADARSAT-2 data to show the effectiveness of the proposed decomposition.

In contrast to the widely used van Zyl received wave polarimetric signature, the Touzi scattered wave signature in term of the total power ($S$0), and the degree of polarization ($p$) is also helpful for target characterization. Although, the van Zyl polarimetric signature includes the contribution of So, and p, the explicit consideration of the two scattered wave parameters (i.e., independent of the received wave polarization basis) can provide additional information about the target. Hence, in this study, we have used both the van Zyl received, and Touzi scattered wave information to characterize scattering from Paddy at a particular phenological stage with C- and L-band full polarimetric SAR data.

In this study, we propose a sequence-to-sequence neural network architecture to jointly estimate the plant area index (PAI) and wet biomass of canola and soybean. The PAI and wet biomass have considerable importance for crop growth stage mapping and monitoring. RADARSAT-2 quad-pol data along within situmeasurements of canola and soybean obtained from the SMAPVEX16 campaign over Manitoba, Canada, are utilized for evaluating the efficiency and accuracy of the proposed estimation methodology. The analysis indicates promising results for the two crops with a correlation coefficient$(r)$in the range of 0.69–0.87. The results also confirm intercorrelation between the PAI and wet biomass for canola and soybean.

In radar polarimetry, incoherent target decomposition techniques help extract scattering information from polarimetric synthetic aperture radar (SAR) data. This is achieved either by fitting appropriate scattering models or by optimizing the received wave intensity through the diagonalization of the coherency (or covariance) matrix. As such, the received wave information depends on the received antenna configuration. Additionally, a polarimetric descriptor that is independent of the received antenna configuration might provide additional information which is missed by the individual elements of the coherency matrix. This implies that existing target characterization techniques might neglect this information. In this regard, we suitably utilize the 2-D and 3-D Barakat degree of polarization which is independent of the received antenna configuration to obtain distinct polarimetric information for target characterization. In this study, we introduce new roll-invariant scattering-type parameters for both full-polarimetric (FP) and compact-polarimetric (CP) SAR data. These new parameters jointly use the information of the 2-D and 3-D Barakat degree of polarization and the elements of the coherency (or covariance) matrix. We use these new scattering-type parameters, which provide equivalent information as the Cloude α for FP SAR data and the ellipticity parameter χ for CP SAR data, to characterize various targets adequately. Additionally, we appropriately utilize these new scattering-type parameters to obtain unique non-model-based three-component scattering power decomposition techniques. We obtain the even-bounce, and the odd-bounce scattering powers by modulating the total polarized power by a proper geometrical factor derived using the new scattering-type parameters for FP and CP SAR data. The diffused scattering power is obtained as the depolarized fraction of the total power. Moreover, due to the nature of its formulation, the decomposition scattering powers are non-negative and roll-invariant while the total power is conserved. The proposed method is both qualitatively and quantitatively assessed utilizing the L-band ALOS-2 and C-band Radarsat-2 FP and the associated simulated CP SAR data.

This article proposes a three-phase three-level cascaded H-bridge (CHB) based shunt active power filter (SAPF) for the management of peak power demand and the mitigation of multiple power quality problems. The proposed topologies are controlled by a sliding-mode control (SMC) for robustness and better dynamic response. The SMC is designed by mathematical modeling of SAPF. The proposed three-level SAPF for the three-wire system is developed by using three H-bridges and three coupling inductors. The same topology can be extended to the four-wire system without using any additional switches. The management of peak power demand of load is achieved by connecting batteries with the dc-link of the H-bridges. The three-wire topology can be used for harmonic mitigation, power factor correction, and compensation of current unbalances. The proposed topology in the four-wire network is required to suppress the neutral current flow in addition to the abovementioned power quality problems. The performances of proposed topologies are verified through MATLAB/ Simulink and validated through the experimental prototype developed in the laboratory.

In radar remote sensing applications, soil moisture retrieval over the vegetated surface is a challenging issue due to complex interaction of radar waves with vegetation layer and the underlying soil. Several studies utilized the Water Cloud Model (WCM) directly or by coupling it with surface inversion models, to compensate vegetation effects while estimating soil moisture. The realization of vegetation component in the original form of WCM utilizes various plant descriptors (e.g., vegetation water content (VWC) and plant area index (PAI)). These descriptors were eventually replaced with vegetation metric obtained from ancillary sources (e.g., the Normalized Difference Vegetation Index -NDVI derived from the optical sensor). To overcome this dependency on ancillary data, we utilize radar derived vegetation descriptors to estimate soil moisture over canola fields. We investigated the performance of WCM for soil moisture retrieval utilizing the PAI and radar derived vegetation descriptors, i.e., the ratio of backscatter intensities (HH/VV and VH/VV) and indices (viz., Radar Vegetation Index (RVI), and Generalized Radar Vegetation Index (GRVI)) in WCM. The radar data derived vegetation descriptors provides encouraging retrieval accuracy with RMSE ranging from 0.04 (for GRVI) to 0.08 m3m-3(for HH/VV). This comparative analysis using different polarizations indicates that the HH polarization outperforms VV, while VH has marginal deviations for all descriptors.

The scattering information from targets is either estimated by fitting suitable scattering models or by optimizing the received wave intensity through the diagonalization of the coherency (or covariance) matrix. In this study, a new roll-invariant scattering-type parameter is introduced, which jointly uses the 3D Barakat degree of polarisation and the elements of the coherency matrix as the received wave information from full-polarimetric (FP) SAR data. This scattering-type parameter is analogous to that of Cloude-Pottier's α for FP SAR data. Furthermore, we utilize this new scattering-type parameter to obtain a unique non-model based three-component scattering power decomposition technique. The powers obtained from the proposed technique are guaranteed to be non-negative, with the total power being conserved. The proposed method is qualitatively and quantitatively assessed using the L-band ALOS-2 and the C-band Radarsat-2 FP SAR data.

In this study, we propose a new vegetation index (DpRVI) for dual polarimetric synthetic aperture radar (SAR) data. The evaluation of this new index is performed with a particular attention towards the preparation of the NASA-ISRO SAR (NISAR) L-band system science objective. The proposed vegetation index is derived for two dual-pol (HH-HV and VV-VH) modes obtained through a simulation from L-band full-pol UAVSAR data. Time-series simulated NISAR data are obtained from the UAVSAR data acquired during the SMAPVEX12 campaign over the CAL/VAL test site in Winnipeg (Canada), to assess the proposed vegetation index. The temporal trend of DpRVI follows the growth stages of canola with a promising correlation of DpRVI with several biophysical variables. Correlation analysis indicates that DpRVI derived for VV-VH mode correlates better with the canola biophysical parameters than the HH-HV mode.

Information from polarimetric synthetic aperture radar (PolSAR) imagery has been used for detecting built-up targets in classification problems, whereas it has been poorly exploited for change detection in multitemporal images. In this letter, we proposed an unsupervised approach for the detection of built-up changed areas from multitemporal full-polSAR images. The approach is based on the automatic thresholding of a novel change index based on the joint use of polarimetric span and average-alpha multitemporal information. The index is proposed for highlighting both constructed and demolished built-up elements. The experimental results on multitemporal UAVSAR images demonstrate that the proposed approach provides high detection accuracy and effectively separates among different types of changes, which is not the case with standard methods.

Built-up (BU) area extraction from remote sensing images is important to monitor and manage urbanization and industrialization. In this letter, we propose two BU area extraction techniques based on the analysis of fully polarimetric synthetic aperture radar (PolSAR) data. Both methods exploit the geodesic distance on the unit sphere in the space of Kennaugh matrices. The first method is based on the three dominant scattering types in the scene and compares them with scattering models; if any of them matches with BU type elementary scattering models, then the pixel is said to belong to a BU area. The second method is based on a novel PolSAR BU index (RBUI) composed by considering scattering mechanisms from BU structures. The two proposed techniques are validated on two different urban scenes, one acquired at C-band by RADARSAT-2 and other at L-band by ALOS-2 SAR sensors.

Crop growth monitoring using compact-pol synthetic aperture radar (CP-SAR) data is gaining attention with the rapid advancements toward operational applications. In this article, we propose a vegetation index for compact polarimetric (CP) SAR data [compact-pol radar vegetation index (CpRVI)]. The CpRVI is derived using the concept of a geodesic distance between the Kennaugh matrices projected on a unit sphere. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix and the Kennaugh matrix of an ideal depolarizer (a realization of vegetation canopy). The similarity measure is then modulated with a scaled quantity derived from the scattering power ratio of the same and opposite sense polarization with respect to the transmitted circular polarization. In this article, we utilize time-series-simulated RADARSAT Constellation Mission (RCM) compact-pol SAR data (RH-RV) obtained from the full-pol RADARSAT-2 observations during the soil moisture active passive (SMAP) validation experiment 2016 (SMAPVEX16-MB) campaign in Manitoba, Canada, to assess the proposed vegetation index. Among the various crops grown in this region, in particular, we analyze the growth stages of wheat and soybean due to their different canopy structures. A temporal analysis of the proposed CpRVI with crop biophysical parameters [the plant area index (PAI) and vegetation water content (VWC)] at different phenological stages confirms the trend of CpRVI with the plant growth. Nevertheless, variations of CpRVI values are apparent with different plant densities for both the crop types. Also, the linear regression analysis confirms that the CpRVI values significantly correlate with PAI (r = 0.72 and 0.85) and VWC (r = 0.62 and 0.75) for both wheat and soybean. We observed good retrieval of PAI and VWC for both wheat and soybean.

We propose a generic scattering power factorization framework (SPFF) for polarimetric synthetic aperture radar (PolSAR) data to directly obtain N scattering power components along with a residue power component for each pixel. Each scattering power component is factorized into similarity (or dissimilarity) using elementary targets and a generalized volume model. The similarity measure is derived using a geodesic distance between pairs of 4×4 real Kennaugh matrices. In standard model-based decomposition schemes, the 3×3 Hermitian-positive semi-definite covariance (or coherency) matrix is expressed as a weighted linear combination of scattering targets following a fixed hierarchical process. In contrast, under the proposed framework, a convex splitting of unity is performed to obtain the weights while preserving the dominance of the scattering components. The product of the total power (Span) with these weights provides the nonnegative scattering power components. Furthermore, the framework, along with the geodesic distance (GD) is effectively used to obtain specific roll-invariant parameters such as scattering-type parameter (αGD), helicity parameter (τGD), and purity parameter (PGD). A PGD/αGDunsupervised classification scheme is also proposed for PolSAR images. The SPFF, the roll invariant parameters, and the classification results are assessed using C-band RADARSAT-2 and L-band ALOS-2 images of San Francisco.

This paper proposes a novel twenty switch three-level cascaded H bridge (CHB) based UPQC for the mitigation of multiple power quality problems. The proposed topology is developed by using five H bridge and two DC link capacitance, so it saves four semiconductor switches and one DC link capacitance compared to the conventional three-level CHB based UPQC. The proposed twenty switches UPQC not only save the power electronics component but also reduce the voltage stress across the switches by using hybrid topology in the shunt part of UPQC. The shunt part of UPQC is controlled by using a modified control strategy, hence it mitigates the voltage unbalances present in the DC link capacitors. The proposed topology is operated as in UPQC-Q for reducing the stress in the shunt part of UPQC, hence the series part of UPQC also uses a modified control circuit. The proposed UPQC mitigates the current harmonics, current unbalances, voltage harmonics, voltage sag, voltage unbalances and reactive power injection. These performances are verified by using the MATLAB/Simulink.

In this paper, a comparative study is reported on Direct Torque Control (DTC) Strategies for Neutral Point Clamped (NPC) inverter driven Interior Permanent Magnet Synchronous Motor drive. In conventional DTC scheme, low number of voltage vectors applied to the machine may cause undesired torque and flux ripples. In this paper, 2-DTC strategies for a three level inverter is implemented in order to have large number of available active voltage vectors. Moreover, the control strategy has the flexibility to split the stator flux locus into large number of sectors. As a consequence, significant reduction in torque and flux ripples is achieved. A modified look up table is established and explained for control strategies where stator flux plane is divided into 6 and 12 sectors for a three level DTC. The results of the proposed strategies are compared with two level DTC. To carry out the analysis, simulation is implemented in MATLAB/Simulink environment.

This work deals with the classification of wheat phenology by regressing the synthetic aperture radar (SAR) backscatter coefficients (VV, VH) to vegetation water content (VWC) and plant area index (PAI) through a representation learning network. The representation network architecture consists of a pair (VV, VH) of two regression layers (VWC, PAI) which finally converge to a classification (crop phenology) layer. The study was conducted with the Sentinel-1 C-band SAR data acquired during the SMAPVEX16 campaign in Manitoba, Canada. Using this framework, the wheat phenology was classified to an accuracy of 86.67%. However, in comparison, the classification accuracy reduced by ~ 20% while using only the backscatter coefficients of (VV, VH) polarization channels. The results obtained from this study justifies the potential of using a representation learning scheme for crop phenology classification with SAR data.

In this study, we propose a vegetation index for compact polarimetric (CP) SAR data (CpRVI) using a geodesic distance between two Kennaugh matrices projected on a unit sphere, as given in Ratha et. al. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix and the Kennaugh matrix of an isotropic depolarizer. The proposed vegetation index is compared with the Radar Vegetation Index (RVI) obtained from RADARSAT-2 full-polarimetric SAR data. We use a time series of simulated compact-pol SAR data (RH-RV) obtained from the RADARSAT-2 data acquired during the SMAPVEX16-MB campaign over the Joint Experiment for Crop Assessment and Monitoring (JECAM) test site in Manitoba, Canada to assess the proposed vegetation index. Among the various crops grown in this region, only the growth stages of soybean are analyzed in this work. The temporal trend of CpRVI follows the growth stages of soybean. Regression analysis shows that CpRVI correlates better with the Plant Area Index (PAI) and Vegetation Water Content (VWC) than RVI.

In this paper, a generic scattering power factorization framework for Polarimetric SAR (PolSAR) data is proposed to directly obtain N scattering power components along with a residue power component for each pixel. Each scattering power component can be factorized into similarity (or dissimilarity) with the utilized scattering models. The similarity measure is derived using a geodesic distance between pairs of 4 × 4 real Kennaugh matrices. In a standard model-based decomposition framework, the 3×3 Hermitian positive semi-definite covariance (or coherency) matrix is expressed as a weighted linear combination of scattering targets. The scattering powers are usually obtained by solving an under-constrained system of equations by enforcing certain assumptions to reduce the number of variables. Moreover, the scattering powers are determined following a fixed hierarchy process. In contrast, under the proposed framework a convex splitting of unity is performed to obtain the weights while preserving the dominance of the scattering components. The product of the total power (Span) with these weights provides the non-negative scattering power components. The scattering power distribution obtained using the proposed framework is assessed over some selected areas from a RADARSAT-2 C-band PolSAR image of San Fran-cisco (SF), USA.

Let me begin by highlighting the tremendous work of the previous Editors-in-Chief: Prof. W. J. Emery, Prof. P. Gamba, and Prof. A. C. Frery in establishing IEEE Geoscience and Remote Sensing Letters (GRSL) as one of the premier journals for short papers. The journal publishes new ideas, results, and formative concepts in remote sensing.

Compact polarimetry (CP) offers a tradeoff with fully polarimetric modes in terms of swath width, power budget, and polarimetric information content. In this letter, a classification comparison is made among real CP, simulated CP (SCP), and quad polarimetric (QP) data acquired from the L-band SAR system onboard the ALOS-2 satellite. The Wishart supervised classification scheme is used to compare data modes over two regions of a mixed test site in India. The quantitative classification assessment indicates that the QP data have higher classification accuracy than any other polarimetric combinations for both regions. The comparative classification accuracy of real versus SCP data is different for the two regions. The overall accuracy of the real CP data is slightly higher ~1% than SCP for region 1, which is dominated by urban and rice classes, whereas it is lower by ~9% for the agricultural crop dominated region 2.

In this letter, we propose a novel vegetation index from polarimetric synthetic-aperture radar (PolSAR) data using the generalized volume scattering model. The geodesic distance between two Kennaugh matrices projected on a unit sphere proposed by Ratha et al. is used in this letter. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix and generalized volume scattering models. A factor is estimated corresponding to the ratio of the minimum to the maximum geodesic distances between the observed Kennaugh matrix and the set of elementary targets: trihedral, cylinder, dihedral, and narrow dihedral. This factor is then scaled and multiplied with the similarity measure to obtain the novel vegetation index. The proposed vegetation index is compared with the radar vegetation index (RVI) proposed by Kim and van Zyl. A time series of RADARSAT-2 data acquired during the Soil Moisture Active Passive Validation Experiment 2016 (SMAPVEX16-MB) campaign in Manitoba, Canada, is used to assessing the proposed RVI.

This paper presents a multiresolution textural approach to change detection in multi-temporal synthetic aperture radar (SAR) images. The proposed approach exploits curvelet and contourlet based multi-scale transforms for SAR data where textural information is extracted at various scales and in different directions in terms of statistical moments and energy to generate the feature vectors. The L1-norm is used to generate the difference image, which is thresholded using the maximum entropy principle to obtain final change detection map. The results are compared with the changes detected by wavelet based textural features. Accuracy assessment is performed for change maps and comparative analysis is carried out in terms of missed changes, false-alarms and overall accuracies. It is found that the proposed method exhibits high change detection accuracy with better edge continuity compared to wavelet based methods.

This paper presents a novel framework for snow cover mapping with multi-basis Poincare sphere parameters obtained from full-polarimetric SAR images in conjunction with an Auto-Encoder neural network. The neural network comprises of two stages where an unsupervised stochastic sampling Auto-Encoder (AE) learns a summarized representation of multi-basis polarimetric SAR data, and a supervised Feed Forward (FF) network performs classification. The proposed algorithm is demonstrated for snow-cover mapping using the Radarsat-2 (FQ-28) C-band full-polarimetric SAR datasets acquired over the Manali-Dhundi region of Himachal Pradesh, India. The results are visualized along with the NDSI-based snow cover map derived from the LANDSAT-8 imagery for the region.

In this paper, a multi-target inversion scheme is adopted for joint estimation of crop biophysical parameters from dual-pol SAR data. The single-output support vector regression (SVR) method is extended to a multi-output support vector regression (MSVR) method to estimate biophysical parameters. The MSVR is implemented for simultaneous retrieval of plant area index (PAI) and crop biomass from the Sentinel-l C-band dual-pol (VV + VH) data. In this particular study, the inversion algorithm is trained and validated for the canola crop using in-situ measurements collected during the Soil Moisture Active Passive Validation Experiment 2016 (SMAPVEX16-MB) Manitoba campaign. The validation results indicate a good correlation coefficient (r) of 0.72 and 0.85, with a RMSE of 0.35 m2m-2and 0.48 kgm-2for PAI and wet biomass respectively. In addition, the mapped PAI and wet biomass values at flowering stage of canola capture the variability in crop growth from Sentinel-l data.

This paper presents a novel scattering power factorization framework in radar polarimetry using a geodesic distance between the 4×4 real Kennaugh matrices of the observed and the elementary targets (viz. dihedral, trihedral, dipole etc.). The framework provides both qualitative and quantitative estimates of the dominance of elementary scattering mechanisms in a pixel. It is also flexible in terms of the number of elementary models against which an observed backscattering may be compared. Under this framework, the observed scattering is evaluated in terms of scattering similarities which provide the dominance of scattering mechanisms. This is then further utilized for a convex splitting of unity to obtain the intermediate weights. This leads to the weights being the product of similarity and dissimilarity of the observed pixel with the elementary scattering models. Finally, these weights are modulated with the total power (Span) to obtain the non-negative scattering powers. The results are shown for full polarimetric single-look ALOS-2 L-band dataset and a multi-look RADARSAT-2 C-band dataset.

A novel tensorization framework is proposed, which utilizes the Kronecker product to combine multifrequency polarimetric synthetic aperture radar data in conjunction with an artificial neural network (ANN) for classification. The ANN comprises of two stages, where an unsupervised stochastic sampling autoencoder learns an efficient representation and a supervised feed forward network performs classification. The proposed framework is demonstrated using multifrequency (C-, L-, and P-bands) data sets collected by the AIRSAR system. The classification performance of single tensor product of dual- and triple-band combinations is evaluated. It is observed that the classification accuracy of the tensor products outperforms single, as well as, the simple augmentation of the frequency bands.

Accurate spatio-temporal information about rice growth is an important factor for agronomic management and regional grain yield estimation. In this letter, a unified framework for monitoring and mapping of rice using dense time-series of Sentinel-1 synthetic aperture radar (SAR) images is proposed. A processing chain for such dense time-series Sentinel-1 images is developed with the Google Earth Engine's cloud computing platform. A dense time-series analysis of backscatter response of rice with different management practices is analyzed. Subsequently, the early and late transplanted rice is classified using a clustering algorithm within this platform. The proposed approach is used to monitor different cultivars of rice in three districts in the state of West Bengal, which is one of the major rice growing regions in India. The classification accuracy is assessed across 150 validation points spanning multiple blocks for the 2017 monsoon season. The Sentinel-1 SAR images acquired up to the early vegetative stage for rice have provided satisfactory classification accuracy with an overall accuracy >85% with κ ~ 0.86 across different management practices throughout the region.

In this letter, we propose a novel technique for obtaining scattering components from polarimetric synthetic aperture radar (PolSAR) data using the geodesic distance on the unit sphere. This geodesic distance is obtained between an elementary target and the observed Kennaugh matrix, and it is further utilized to compute a similarity measure between scattering mechanisms. The normalized similarity measure for each elementary target is then modulated with the total scattering power (Span). This measure is used to categorize pixels into three categories, i.e., odd-bounce, double-bounce, and volume, depending on which of the above scattering mechanisms dominate. Then the maximum likelihood classifier of Lee et al. based on the complex Wishart distribution is iteratively used for each category. Dominant scattering mechanisms are thus preserved in this classification scheme. We show results for L-band AIRSAR and ALOS-2 data sets acquired over San Francisco and Mumbai, respectively. The scattering mechanisms are better preserved using the proposed methodology than the unsupervised classification results using the Freeman-Durden scattering powers on an orientation angle corrected PolSAR image. Furthermore: 1) the scattering similarity is a completely nonnegative quantity unlike the negative powers that might occur in double-bounce and odd-bounce scattering component under Freeman-Durden decomposition and 2) the methodology can be extended to more canonical targets as well as for bistatic scattering.

DC/AC microgrid is independent control grid form of integrate distributed energy systems with utility powers systems; will powerfully understand the value and profit of the distributed energy resources. In this paper, the AC/DC micro-grid mostly contains of six parts: a solar source (P V source), Wind source, a Boost converter, BESS, a Buck-Boost converter (bidirectional), and a bi-directional DC/AC which is associated to the utility grid. The DC/AC microgid have two operation mode (islanded or grid disconnect and grid-connected). A simple drop control technique is used for controlling different entities of micro grid. The control approach of DC/AC is implemented by matlab or simulink models. The simulation result validates the effectiveness of proposed controller.

Urban change detection is an important part of monitoring operations and disaster relief efforts. However, often sufficient ground truth data is not available to use traditional supervised machine learning techniques. In this paper, a novel Deep Learning based weakly-supervised framework for urban change detection using multi-temporal polarimetric SAR data is proposed. A modified unsupervised stacked auto-encoder stage is used to learn an efficient representation of the multi-temporal polarimetric information. Then a label aggregation is performed in the feature space before classification by a multi-layer perceptron. The proposed methodology is validated on a L-band UAVSAR dataset acquired over Los Angeles, CA and performs accurately and effectively with a low false alarm rate.

Synthetic aperture radar (SAR) has shown promising results in characterizing different crops. Multi-temporal SAR data is often useful for studying the sensitivity of the electromagnetic (EM) waves to the structural and the dielectric variation of both crops and the underlying soil at different phenological stages. Physical information about crops and soil can be interpreted in terms of scattering mechanisms using polarimetric descriptors. In this study, the potential of the Touzi eigenvalue-eigenvector decomposition parameters is analyzed for Leaf Area Index (LAI) and soil moisture variations over the phenophases of wheat crop. The AgriSAR 2006 campaign E-SAR L-band full polarimetric SAR data were used in this study. It was observed that the crop phenological parameters could be justifiably associated with the two dominant Touzi parameters, symmetric scattering type magnitude (αs1) and the phase (ϕαs1), for phenological assessment.

Change detection in large urban areas is an application with increasing relevance. In this domain, Polarimetric SAR (PolSAR) sensors are receiving more attention recently. The enhanced polarimetric information provides useful features which can describe multi-temporal changes. In this work, we aim at introducing an approach for unsupervised change detection with focus on built-up areas that relies on the polarimetric information. This approach is based on the analysis of the multi-temporal α feature obtained from the Cloude-Pottier eigenvalue/eigenvector decomposition. Large differences in the α values can be associated to changes in the dominant scattering mechanism. These are likely to be associated to buildings when built-up areas are considered. Changes are detected according to an automatic and unsupervised approach. Validation is conducted on a pair of UAVSAR images acquired over Los Angeles, USA. Preliminary results highlight the effectiveness of proposed approach.

Change over the Himalayan terrain in the form of seasonal snow precipitation is an inevitable phenomenon. Mapping of snow cover in this region is a critical task, since meltwater emanating from the snowfields and glaciers serves as a source to several major Asian river systems. In this regard, a snow cover mapping technique is proposed in this letter by exploiting the ratio of the seasonal variation of copolarized (hh-vv) correlation coefficient and the total scattering power. This ratio provides a very efficient index for snow characterization. The difference image is obtained by temporal (winter-summer) ratioing of this index. The snow cover map is obtained by thresholding the difference image using the standard method of Otsu. The proposed algorithm is validated using the temporal RADARSAT-2 (FQ-28) C-band full-polarimetric synthetic aperture radar data sets acquired over the Manali-Dhundi region of Himachal Pradesh, India. The results are explicitly validated with in situ observatory measurements and compared with the normalized difference snow index-based snow cover maps derived from the LANDSAT-8 optical satellite images.

A novel technique to generate the difference image (DI) in change detection analysis for polarimetric SAR (PolSAR) data is proposed. Unlike the standard methods, viz., band difference or intensity/amplitude ratioing, the proposed technique utilizes the full vector nature of multitemporal PolSAR data. In this data, a pixel is characterized by a 4 × 4 Kennaugh matrix. The geodesic distance (GD) on an unit sphere is utilized to define the distance between the Kennaugh matrices of the three elementary targets (trihedral, dihedral, and 45° rotated dihedral about the radar line of sight) producing canonical scattering mechanisms and the observed Kennaugh matrix. Three absolute differences of the GD from respective elementary targets are obtained for time instants t1and t2. The DI is then the maximum among the three quantities. The proposed technique is applied to two scenes obtained from the L-band UAVSAR data characterizing changes due to urbanization. The principal component analysis with k-means clustering proposed by Celik is used to obtain the binary change map. The proposed differencing method performs better than the single-channel intensity band ratio and the total power ratio for time instants t1and t2. The detection rate with the proposed technique is 6% and 20% better than the ratio methods for the two data sets, respectively, with the higher κ value as a measure of performance evaluation.

This paper proposes an iterative unsupervised Markov Random Field (MRF) based segmentation technique for polarimetric Synthetic Aperture Radar (SAR) image using the optimized scattering mechanism similarity parameters. Parameter estimation for the MRF model is generally performed from the available training data in order to perform tasks including semantic image segmentation. Since the current scenario is entirely unsupervised, the parameter estimation is performed iteratively using the Expectation Maximization (EM) technique considering the classes are distributed according to Gaussian functions. Further, we model the pairwise potential of the MRF cost function using a weighted combination of the similarity parameters. Results obtained on a fully polarimetric SAR data establishes the potential of such unsupervised random field models for analyzing SAR data effectively.

We analyzed the surface elevation change and geodetic mass change of Gangotri glacier, over the period 2011 and 2013, utilizing high horizontal and vertical resolution topographic data, acquired by bistatic radar interferometry of the TanDEM-X/TerraSAR-X satellite formation. Short term investigation of surface elevation change of glaciers at reasonably good accuracy is possible by using multi temporal TanDEM-X data. The surface elevation change further can be converted into glacier volume change and mass change. The mass change of glaciers is direct response of climate change and hence can be taken as a proxy to study climate change. The study area includes the Gangotri group of glaciers, located in the Central Himalaya, India.

In this paper, we propose an effective wet snow mapping method with focus on the incident angle of microwave. Surface scattering is dominant for both wet snow and bare ground. However, it is expected that the characteristic of the wet snow scattering is different from the bare ground one according to the variation of dielectric constant. At the same time, surface scattering characteristics, especially depolarization, also depend on the incident angle. First, we evaluate numerically the degree of polarization of horizontal incident wave as an example with a simplified integral equation model (IEM). We also examine real data of full polarimetric synthetic aperture radar (PolSAR). The results shows that the degree of polarization depends on the difference of incident angles rather that of dielectric constants. Then we conduct wet-snow mapping by supervised learning with teacher areas for large / small incident angles and snow / bare ground. The mapping result agrees well with the estimation by optical data. It is found important to take into account the incident angle in snow mapping.

Extraction of polarimetric information from SAR data is one of the most important issues in SAR applications. Since fully polarimetric SAR data and model-based scattering power decomposition are now available, its real utilization becomes important topic. In this presentation, some data sets from PiSAR-L2, ALOS, ALOS-2 systems are shown using the existing model-based scattering power decompositions. The observation areas are chosen so that each scattering mechanism correspond specific scattering power utilization, i.e., landslide area for the surface scattering, urban expansion for the double bounce scattering, deforestation for the volume scattering. The final color-coded images provide us with direct way to understand the scattering scenario in the real world.

Polarimetric synthetic aperture radar is expected to distinguish wet snow from bare ground. However, since both of them show surface scattering, which is sensitive to incidence angle, it often fails in the distinction in mountainous areas. In this letter, we propose an adaptive distinction method using quaternion neural networks. In the ALOS-2 data, we find a monotonic and nonlinear dependence of the degree of polarization on the incidence angle. Then, we feed multiple-incidence-angle teacher information in the learning process. The distinction results of the proposal present higher accuracy than those of the conventional Wishart distinction and a quaternion neural network without the incidence angle information.

In this paper, direct matrix converter (DMC) based wind energy systems equipped with Permanent Magnet Synchronous Generators (PMSG) is proposed. To extract the maximum available power from the wind, the Matrix Converter (MC) is controlled using the venturini based algorithm together with the voltage oriented vector control (VOC) scheme to regulate active and reactive power (Power Regulation Technique). In this scheme, the maximum extractable power is derived from wind turbine power curves by using Maximum Power Point Tracking (MPPT) is set as reference active power while reference for reactive power is taken as zero. The change in generated power due to change in wind speed are processed through proportional & integral (PI) controllers to regulate the voltage gain of Matrix Converter (MC). In result, the desired regulation of powers is carried out. To evaluate the performances of proposed scheme, Matlab/Simulink based model is tested under varying wind speed. From the simulation results it is confirm that MC with adequate input filters and proposed control scheme is able to extract the maximum power from the wind energy conversion system (WECS) and fed directly to the existing grid at desired output frequency and voltage at nearly unitary input power factor.

A Z-source inverter (ZSI) has a unique ability to achieve single stage voltage buck-boost operation for energy conversion. This paper presents a three level ZSI using a single impedance network for photovoltaic (PV) application. A single LC impedance network is used between DC voltage source and neutral-point-clamped (NPC) inverter to achieve the desired stepped-up and stepped-down output voltage level. Alternative phase opposition and disposition (APOD) modulation technique with proper triplen offset and appropriate addition of time delay/advance is used to achieve the required boost in DC link voltage. A traditional MPPT technique is used to introduce a shoot-through interval in switching waveform to extract the maximum power from the PV panel. Traditional MPPT technique does not allow to boost the Z-network capacitor voltage more than the maximum power point (MPP) voltage of the PV array. This paper also presents a unified voltage control technique to track the MPPT and also maintains the desired Z-source capacitor voltage level. The design, implementation and control of single impedance network based multilevel ZSI for photovoltaic application is discussed and their MATLAB/Simulink simulation and results are presented in the later section of the paper to validate proposed control scheme in PV application.

The urban classification of PolSAR images is made difficult by the characteristic of a rotated target to exhibit volume scattering. In this paper we use a deep learning technique in conjunction with some statistical parameters to learn to classify urban areas irrespective of the rotation. The learning algorithm was trained to differentiate urban from non-urban areas and was able to achieve a 8.5834% validation accuracy and 6.554% test accuracy.

This letter addresses the problem of unsupervised land-cover classification of remotely sensed multispectral satellite images from the perspective of cluster ensembles and self-learning. The cluster ensembles combine multiple data partitions generated by different clustering algorithms into a single robust solution. A cluster-ensemble-based method is proposed here for the initialization of the unsupervised iterative expectation-maximization (EM) algorithm which eventually produces a better approximation of the cluster parameters considering a certain statistical model is followed to fit the data. The method assumes that the number of land-cover classes is known. A novel method for generating a consistent labeling scheme for each clustering of the consensus is introduced for cluster ensembles. A maximum likelihood classifier is henceforth trained on the updated parameter set obtained from the EM step and is further used to classify the rest of the image pixels. The self-learning classifier, although trained without any external supervision, reduces the effect of data overlapping from different clusters which otherwise a single clustering algorithm fails to identify. The clustering performance of the proposed method on a medium resolution and a very high spatial resolution image have effectively outperformed the results of the individual clustering of the ensemble.

An adaptive general four-component scattering power decomposition method (AG4U) is proposed in this letter. The degree of polarization mis used as a criterion for the adaptive nature of the proposed decomposition. In this method, one among the two complex special unitary transformation matrices is chosen to transform a real unitary rotated coherency matrix based on the largest value of m. This transformed matrix is then utilized for the existing Yamaguchi et al. four-component decomposition scheme with an extended volume scattering model. The proposed decomposition is applied to Radarsat-2 full-polarimetic C-band data over San Francisco and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) full-polarimetric L-band data over the Hayward Fault in California. The scattering powers estimated from the decomposition techniques of Yamaguchi et al. (Y4O), Singh et al. (G4U), and AG4U are compared. AG4U shows appreciable improvements in the scattering powers, particularly in urban areas oriented about the radar line of sight compared with the Y4O and G4U decompositions. It also shows reduced percentage of pixels with negative powers considerably compared with the Y4O decomposition.

This paper addresses the problem of land-cover classification of remotely sensed image pairs in the context of domain adaptation. The primary assumption of the proposed method is that the training data are available only for one of the images (source domain), whereas for the other image (target domain), no labeled data are available. No assumption is made here on the number and the statistical properties of the land-cover classes that, in turn, may vary from one domain to the other. The only constraint is that at least one land-cover class is shared by the two domains. Under these assumptions, a novel graph theoretic cross-domain cluster mapping algorithm is proposed to detect efficiently the set of land-cover classes which are common to both domains as well as the additional or missing classes in the target domain image. An interdomain graph is introduced, which contains all of the class information of both images, and subsequently, an efficient subgraph-matching algorithm is proposed to highlight the changes between them. The proposed cluster mapping algorithm initially clusters the target domain data into an optimal number of groups given the available source domain training samples. To this end, a method based on information theory and a kernel-based clustering algorithm is proposed. Considering the fact that the spectral signature of land-cover classes may overlap significantly, a postprocessing step is applied to refine the classification map produced by the clustering algorithm. Two multispectral data sets with medium and very high geometrical resolution and one hyperspectral data set are considered to evaluate the robustness of the proposed technique. Two of the data sets consist of multitemporal image pairs, while the remaining one contains images of spatially disjoint geographical areas. The experiments confirm the effectiveness of the proposed framework in different complex scenarios.

The angle of rotation (θ) of any object about the line of sight (LOS) is known as the polarization orientation angle (OA). The OA is found to be non-zero for undulating terrains and man-made targets oriented away from the radar LOS. This effect is more pronounced at lower frequencies (eg. L- and P-bands). The OA shift is not only induced by azimuthal slope but also by range slope. The OA shift increases the cross-polarization (HV) intensity and subsequently the co-variance or the coherency matrix becomes reflection asymmetric. Compensating this OA prior to any model-based decomposition technique for geophysical parameter estimation or classification is crucial. In this paper a new method has been proposed for OA estimation based on a stochastic distance. The OA is estimated by maximizing the Hellinger distance between the un-rotated and rotated diagonal elements of the coherency matrix.

In this paper, a new snow wetness estimation methodology is proposed for dual-coherent polarimetric Synthetic Aperture Radar (SAR) data. Surface and volume are the dominant scattering components in the wet-snow conditions. These components, with a limit of penetration depth of high frequency SAR, have been taken into account to estimate the snow-pack wetness. In this new methodology, snow surface wetness has been estimated using the IEM scattering model and snow volume wetness has been estimated under the Rayleigh scattering assumption. The estimated snow wetness is validated using the in-situ field measurements, which were collected synchronous with the satellite pass. In this study we have used dual-coherent TerraSAR-X data acquired over Solang, on 23 January 2009, Himachal Pradesh, India. Typically the snow wetness ranges from 0% to 15% by volume. On comparison with ground measurements, the proposed method shows that the mean absolute error in snow wetness inferred from the SAR imagery was 1.63% by volume.

In this paper we have classified Polarimetric Synthetic Aperture Radar (PolSAR) data using the Random Forest (RF) classifier. The variables were ranked using the mean decrease in accuracy permutation method for each terrain class. RADARSAT-2 (RS-2) data acquired over Mumbai, India was used in this study. This technique is able to efficiently classify the dataset, as well as rank the parameters used in that classifier.

Edge detection in SAR images has always been a challenge due to the effects of random interference of coherent signal. Unlike optical images, boundary delineation of regions is relatively ineffective for SAR images. The usual edge detectors, successful with incoherent images, yield poor results when applied to radar images, especially those with a small number of looks. Radon spectrum can be used to find the local orientations in an image which embed the edge information [1]. In this paper, we demonstrate the capability of Radon transform to efficiently extract edges from a SAR image covering agricultural landform. One approach to edge detection in polarimetric SAR images is to perform the edge detection separately for each of the polarization channels and subsequently combine the results using a fusion operation [2] [3]. We have made use of HH, HV and VV channels and fused the edge maps of these individual bands with Boolean ‘AND’ operator. This type of fusion has preserved the prominent edge information reasonably while suppressing spurious ones.

High Circular Polarization Ratio (CPR) was thought to be a robust diagnostic of water-ice on the lunar surface. Recent researches have reported such findings on walls, floors, and proximal ejecta of impact craters, as well as on sunlit zones. These signatures could not be explained with water-ice as the probable cause. In an attempt to explain such sightings, this paper portrays the character of radar waves backscattered from the lunar surface. This characterization is performed with the aid of daughter products derived from the Stokes vector.

Snow wetness is a very important parameter for forecasting snow avalanche and for snow melt run off modeling in cragged areas specifically for Himalayan regions of India. In this paper, a new snow wetness estimation approach is used for fully polarimetric Synthetic Aperture Radar (SAR) data. In this new methodology, Freeman surface scattering and Cloude volume scattering components are introduced which account for all independent relative polarimetric phase parameters of the coherency matrix. Snow particle has been considered to be of spheroidal shape in volume scattering model. The estimated snow wetness is validated using the field data, which was collected, synchronized with the satellite pass. The results were also compared with the Shi and Dozier [1] inversion model based snow wetness estimation.

The launch of RISAT-1 Indian remote sensing satellite on 26thApril 2012, made it possible to collect hybrid polarimetric data from a space-borne sensor. The RISAT-1 C-band compact polarimetry data acquired over Mumbai is analyzed and assessed for classification of various land features and also compared with other fully polarimetric spaceborene SAR data sets. For better comparison, RISAT-1 C-band and RADARSAT-2 C-band simulated compact polarimetric data is classified and compared.