A new Polarimetric Interferometry Inverse Synthetic Aperture Radar (Pol-InISAR) 3D imaging method for non-cooperative targets is proposed in this paper. 3D imaging of non-cooperative targets becomes possible by combining additional information of interferometric phase along with conventional 2D ISAR imaging. In the previously reported single-polarimetry InISAR based 3D imaging, only a single-channel based interferometric phase is available that can be exploited to reconstruct the 3D ISAR image. This limits the ability to obtain a full target's scattering response and therefore limits the estimation of an accurate interferometric phase. To overcome this constraint, full-polarimetry information is being exploited in this paper, which allows to select the optimal polarimetric combination through which the highest coherence can be obtained. A higher coherence leads to a reduction (optimally a minimization) of the phase estimation error. Consequently, with an optimal phase estimation, an accurate 3D imaging of the target is possible. To validate this proposed Pol-InISAR based 3D imaging approach, both simulated and real datasets are taken under consideration.

In this paper, we present an unsupervised model-based decomposition technique for the analysis of hybrid-pol datasets. The proposed approach develops a hybrid-pol version of full-pol three-component scattering model by deriving all decomposition parameters in terms of the hybrid-pol Stokes parameters. By using these decomposition parameters, the contribution of three basic scattering mechanisms, namely surface-scattering from Bragg surface region, dihedral-scattering from urban region and volume-scattering from forest region, are estimated. The decomposition parameters take surface dielectric properties under consideration that makes the proposed method suitable to provide more realistic representation of the Earth's surface. To estimate the efficacy of the proposed technique, the hybrid-pol data acquired over Mumbai city using ISRO's RISAT-1 satellite is used. For comparative analysis, the proposed technique is compared with the conventional decomposition techniques, such as $m-\delta$, $m-\dot{\alpha }$, and modified $m-\chi$ .

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 the Biot crater region. Various small areas possessing different possible surface characteristics, situated inside and outside the Biot crater , are being analyzed for validation.

Synthetic Aperture Radar (SAR) remote sensing is a state-of-the-art tool for snow monitoring and snow parameters estimation. SAR remote sensing-based techniques, such as interferometric SAR (InSAR) and Polarimetric SAR (PolSAR) have already proven useful in the estimation of geophysical parameters of snow. InSAR-based techniques utilize interferometric phase information from repeat-pass datasets for snow parameters retrieval. During the monitoring of snow, the large temporal gap between the repeat passes results in the temporal decorrelation in the snowpack, which leads to the loss of interferometric coherence. Hence, there is a need for a technique for snow parameters estimation, which can work with zero temporal baseline datasets. This study works on the development of a Polarimetric SAR Interferometry (PolInSAR) based modelling approach for snow-depth estimation using TerraSAR-X/TanDEM-X datasets acquired in the pursuit monostatic mode (temporal baseline = 10 seconds). The study area of this work is the Manali region of Himachal Pradesh situated in the Beas basin. Multi-temporal analysis of the snow-depth variation is executed utilizing the two pursuit-monostatic TanDEM-X interferometric quad-pol dataset pairs of the dates 21 January 2015 and 22 January 2015. In this study, the PolInSAR-based Coherence Amplitude Inversion modelling approach is used for the snow-depth retrieval. The magnitude of the complex interferometric coherence is used during the modelling implementation. The snow extinction coefficient is estimated and used as an input during PolInSAR modelling. Further, a comparison of the calculated volume coherence magnitude and the observed volume coherence magnitude is done during model implementation for the snow-depth estimation. The snow depth is estimated at a resolution of 15 m × 15 m in range and azimuth directions respectively. The estimated snow depth for both the dates shows a precise correlation with the ground datasets. The rise in model retrieved snow-depth value from 0.84 m to 1.24 m is observed during the period. The retrieved results were validated using the ground data of snow depth from the Automatic weather station (AWS) of Snow and Avalanche Study Establishment (SASE), Defence Research and Development Organization (DRDO), and Indian Institute of Remote Sensing (IIRS) installed in the Dhundi region of the study area for same dates.

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.

The analysis of hybrid-polarimetric synthetic aperture radar (hybrid-pol SAR) data can be carried out using two different types of analytical approaches. The first approach is by implementing direct-technique (DT), which directly takes hybrid-pol Stokes parameters as input. The second approach is reconstruction-based-technique (RBT), where the pseudo quad-polarimetric (quad-pol) data is reconstructed from the hybrid-pol measurements. Methods under DT and RBT categories have their own strengths and weaknesses, which are thoroughly investigated in this paper. Also, a comparative analysis of these methods is carried out on the basis of their ability for scattering type discrimination and land-cover classification using synthesized as well as true hybrid-pol data. Moreover, a new entropy (H)-based RBT approach is proposed in this paper which is being compared with the state-of-the-art techniques.