In the current literature, the linear cross-polarization term, denoted as $\left\langle {{{\left| {{S_{HV}}} \right|}^2}} \right\rangle $, is often considered a lost parameter in hybrid-polarimetry (hybrid-pol) SAR. This assumption is based on the belief that even with the reflection-symmetry condition (which holds for most natural surfaces), five parameters are needed to compute $\left\langle {{{\left| {{S_{HV}}} \right|}^2}} \right\rangle $, whereas hybrid-pol can only measure four parameters, leading to an underdetermined solution. We respectfully disagree with this viewpoint and assert that four parameters are indeed sufficient for accurately calculating $\left\langle {{{\left| {{S_{HV}}} \right|}^2}} \right\rangle $. Accordingly, we have derived a mathematical relationship under the reflection symmetry condition that provides a closed-form solution for calculating $\left\langle {{{\left| {{S_{HV}}} \right|}^2}} \right\rangle $ from hybrid-pol SAR datasets. This has been validated through both theoretical analysis and practical assessments using ALOS PALSAR L-band data

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.

This article presents the development and first-time implementation of an IoT–edge–AI–cloud architecture in an actual landslide location for real-time monitoring and prediction. The proposed architecture benefits the time-critical landslide application by introducing artificial intelligence (AI) and decision-making at the edge of the network. This architecture can address the issues related to network, data packet drops, and device overload while optimizing energy consumption, response latency, and prediction accuracy, all simultaneously. A data offloading scheme is implemented to address the issue of data-packet drops by the IoT-end nodes. This architecture employs an incremental learning approach that periodically retrains the AI model at the edge using real-time data to optimize the prediction accuracy, thus reducing cloud dependency. Compression techniques are also implemented on the edge server to develop light-weighted AI models that can easily run on resource-constrained edge devices.

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.

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.

In the application of oil-spill monitoring, the satellite revisit time needs to be as short as possible to identify minor spills before they can cause widespread damage. Simultaneously, it is required to capture a sufficient amount of information about the surface to clearly distinguish between oil-spilled and oil-free sea regions. The Hybrid-polarimetry (hybrid-pol) synthetic aperture radar (SAR) system can be exploited for such capabilities. However, limited hybrid-pol based oil-spill descriptors are reported in the literature in comparison to rich sets of full-polarimetry (full-pol) based descriptors. In this letter, we establish a direct relation between hybrid-pol data and full-pol data under reflection-symmetry condition. Consequently, through the proposed work, the rich sets of full-pol based oil-spill descriptors can be derived directly from the hybrid-pol datasets. For the validation of the proposed work, L-band ALOS PALSAR and UAVSAR datasets acquired over the Gulf of Mexico have been used.

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.

For more than four decades, it has been known that the dielectric constant of the lunar surface can be retrieved from the Fresnel reflection coefficients. However, theoretical models have met with limited success in validating laboratory test results from the Apollo missions to date. This paper is the first study to focus on the use of high-resolution full-polarimetric synthetic aperture radar datasets for the retrieval of the dielectric constant of the lunar surface from the Fresnel reflection coefficients. We initially show that it is possible to retrieve the lunar dielectric constant via the classical Freeman-Durden Decomposition (FDD). The performance of the FDD algorithm is found to be unacceptable over regions with surface slopes and craters, and for sub-surface soil samples. Accurate estimation is not possible by simply replacing the volume scattering model in the FDD with popular and widely used volume scattering models. Therefore, a model-based three-component decomposition (TCD) algorithm for a robust retrieval of the lunar dielectric constant is proposed. The proposed TCD algorithm implements an efficient branching condition combined with double unitary matrix rotations and provides exceptionally accurate dielectric constant estimation. The proposed TCD algorithm is validated by using L band full-polarimetric datasets acquired by the Chandrayaan-2 mission over Apollo 12, Apollo 15, and Apollo 17 landing sites. Comparisons are also made with other three-component decomposition algorithms. Excellent agreement between the estimated values by the proposed TCD algorithm and the reference values for the dielectric constant, available from the literature, has been observed.

A new dielectric constant estimation approach for the lunar surface using Mini-RF synthetic aperture radar (SAR) data and Chandrayaan-2 SAR data is presented in this work. Both the SAR systems are based on a hybrid-polarimetry (hybrid-pol) configuration that transmits a circularly polarized wave and coherently measures the backscattered wave by dual orthogonal linearly polarized channels. From the three-component hybrid-pol SAR decomposition technique, the ratio of the Fresnel reflection coefficients for horizontal and vertical polarization transmission can be estimated. This ratio, which is referred to as the co-polarization ratio, is used by the proposed methodology to find the real value of the dielectric constant. For performance validation, the proposed method is implemented on the Mini-RF hybrid-pol SAR data acquired over Apollo 17 landing sites. The values of the real part of dielectric constant are estimated for six different regions covering the collection sites of six Apollo 17 samples: 72 441, 73 241, 74 241, 75 081, 76 001, and 79 135. The results obtained using the proposed method are found to be in good agreement with the laboratory-measured results of the corresponding samples. Furthermore, the proposed methodology is also implemented on the Chandrayaan-2 hybrid-pol SAR data acquired over theBiot craterregion. Various small areas possessing different possible surface characteristics, situated inside and outside theBiot crater, are being analyzed for validation.

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.

Progression towards the different scattering models has fulfilled some of the awaited requirements in the physical model-based scattering power decomposition techniques. Among these, the complete utilization of terrain information has recently been achieved by the development of helix and compounded scattering components. However, the achievement comes up with the expense of some adverse effect on the decomposition result. As occasionally, the power associated with any particular component gets overestimated. Apart from that, an enhancement in the percentage of negative scattering pixels has also been observed as the advancement propagates. Paper briefly concentrated on the analysis of decomposed scattering power and negative scattering pixels on the ALOS PALSAR 2 datasets over the San Francisco Bay Area, CA, USA, when being implemented under the different decomposition strategies.

In this letter, a texture classification-based nonlocal means polarimetric SAR (NLM PolSAR) filter is introduced and named as texture classification-based filter (TBF). In this process, a classification algorithm that identifies the data into textural variations and heterogeneity due to speckle noise is presented. Also, a similarity metric is derived to estimate the patch similarity of K-distributed covariance matrices. Hence, a filter is proposed that processes the classified data suitably using NLM patch-based filters with either K- or Wishart distribution patch similarity metrics. The filtering performance of TBF is being analyzed on full-pol single-look RADARSAT-2 and four-look Airborne Synthetic Aperture Radar (AIRSAR) data.

In this letter, a special unitary SU(3) matrix group is exploited for coherency matrix transformations to decouple the energy between orthogonal states of polarization. This decoupling results in the minimization of the cross-polarization power along with the removal of some off-diagonal terms of coherency matrix. The proposed unitary transformations are utilized on the basis of the underlying dominant scattering mechanism. By doing so, the reduced power from the cross-polarization channel is always concentrated on the underlying dominant co-polar scattering component. This makes it unique in comparison to state-of-the-art techniques. The proposed methodology can be adopted to optimize the coherency matrix to be used for the model-based decomposition methods. To verify this, pioneer three-component decomposition model is implemented using the proposed optimized coherency matrix of two different test sites. The comparative studies are analyzed to show the improvements over state-of-the-art techniques.

The speckle filtering is an essential preprocessing step of polarimetric SAR (PolSAR) data processing. The PolSAR speckle filters perform generally better on multilook datasets. The multilooking is performed to reduce speckle and improve parameter estimation but it also degrades resolution of the datasets. The speckle filtering after multilooking further degrades the resolution. The analysis on suitability of speckle filtering algorithms for single-look data is absent in the literature. In this paper, state-of-the-art PolSAR speckle filters are analyzed for the single-look PolSAR data. The single-look Monte Carlo simulated and real RADARSAT-2 PolSAR data are used for performance analysis.

In this letter, we investigate the existing branching conditions used to solve the unknown model-coefficients of modelbased decomposition methods and show that they are less efficient in discriminating between dominant surface and dihedral scattering mechanisms. The discrimination ability of the branching conditions further deteriorates when the target has some random slope and orientation. This greatly suppressed the performance of the model-based decomposition methods. To overcome this problem, we propose an efficient alternate to existing branching conditions of model-based methods. The proposed branching condition is based on the value of the alpha (α) angle derived from the eigenvector analysis of the measured coherency matrix. The roll-invariance property of α angle makes it work efficiently even in the sloped and oriented areas. The proposed concept is experimentally validated over three different polarimetric synthetic aperture radar (PolSAR) data sets. The effectiveness of the α angle is analyzed and compared with the other branching conditions in terms of ability to discriminate between dominant surface and dihedral scattering mechanisms. The experimental results on different PolSAR data sets clearly demonstrate that by replacing the existing branching conditions with the α angle, the performances of the model-based decomposition methods are significantly improved.