Arnab Muhuri

Modifying the Yamaguchi Four-Component Decomposition Scattering Powers Using a Stochastic Distance

Model-based decompositions have gained considerable attention after the initial work of Freeman and Durden. This decomposition, which assumes the target to be reflectionsymmetric, was later relaxed in the Yamaguchi et al. decomposition with the addition of the helix parameter. Since then, many decomposition have been proposed where either the scattering model was modified to fit the data or the coherency matrix representing the second-order statistics of the full polarimetric data is rotated to fit the scattering model. In this paper, we propose to modify the Yamaguchi four-component decomposition (Y4O) scattering powers using the concept of statistical information theory for matrices. In order to achieve this modification, we propose a method to estimate the polarization orientation angle (OA) from full-polarimetric SAR images using the Hellinger distance. In this method, the OA is estimated by maximizing the Hellinger distance between the unrotated and the rotated T33 and the T22 components of the coherency matrix [T]. Then, the powers of the Yamaguchi four-component model-based decomposition (Y4O) are modified using the maximum relative stochastic distance between the T33 and the T22 components of the coherency matrix at the estimated OA. The results show that the overall double-bounce powers over rotated urban areas have significantly improved with the reduction of volume powers. The percentage of pixels with negative powers have also decreased from the Y4O decomposition. The proposed method is both qualitatively and quantitatively compared with the results obtained from the Y4O and the Y4R decompositions for a Radarsat-2 C-band San-Francisco dataset and an UAVSAR L-band Hayward dataset.

PolSAR Wet Snow Mapping With Incidence Angle Information

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.

Seasonal Snow Cover Change Detection Over the Indian Himalayas Using Polarimetric SAR Images

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

Characterization of backscattered radar waves from the lunar surface

(hh{-}vv)

Proposal of wet snowmapping with focus on incident angle influential to depolarization of surface scattering

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.

Glacier surface velocity estimation using stokes vector correlation

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.

A new compact polarimetric SAR decomposition technique

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.

Orientation angle estimation from PolSAR data using a stochastic distance

Cryosphere plays a crucial role in regulating local and global climate. Glaciers form an important component of this frozen part of the Earths system. They exist over a prolonged period and are largest reservoir of freshwater on Earth. The meltwater from the glaciers during warmer seasons contribute to the river systems in absence of other sources. The run-off is also useful for agriculture, power generation, and is rich in alluvium. Retreating glaciers gives rise to pro-glacial lakes formed by damming action of moraine or ice. Rupturing of ice dams have caused serious damage to infrastructure and human lives in the past. Such useful and dynamic characteristics of a glacier motivate us to study its movement. Monitoring glaciers through in-situ field measurements is a cumbersome process. Over the past decade, glaciers have been repeatedly observed through microwave sensors on-board various satellites. Various techniques have been proposed in the literature to estimate glacier velocity using microwave observations. Over the recent past, the trend in glacier velocity monitoring has shifted from interferometric tracking to intensity tracking. In this paper, we propose a method based on Stokes vector correlation of time lapse microwave observations. This method is proposed as an improvement over the conventional intensity correlation technique.

Snow Cover Mapping Using Polarization Fraction Variation With Temporal RADARSAT-2 C-Band Full-Polarimetric SAR Data Over the Indian Himalayas

In this paper, we have proposed a new decomposition technique for compact polarimetric (CP) synthetic aperture radar (SAR) data. In the proposed decomposition, the odd and the even bounce scattering powers and are, respectively, obtained by combining the powers received in the opposite-sense circular (OC) polarization and the same-sense circular (SC) polarization transmitted with the polarized power fraction (). The volume scattering power is obtained by combining the total power with the unpolarized power fraction (). The parameter is a function of both the transmitting and the receiving ellipticities () and orientations (). These parameters thus provide a wider degree of freedom to accommodate a range of scattering mechanisms which are not reflected in the existing approaches. The proposed method is applied on simulated CP-SAR data obtained from full-polarimetric E-SAR (Experimental Synthetic Aperture Radar) and AIRSAR (Airborne Synthetic Aperture Radar) L-band data sets. The proposed decomposition shows appreciable improvements in the scattering powers compared to the and the decompositions.

Scattering Mechanism Based Snow Cover Mapping Using RADARSAT-2 C-Band Polarimetric SAR Data

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.