Crop characterization using an improved scattering power decomposition technique for compact polarimetric SAR data Abstract   Crop characterization using CompactPol Synthetic Aperture Radar (CPSAR) data is of prime interest with the rapid advancements of SAR systems towards operational applications. It is noteworthy that as a good compromise between the dual and quadpolarized SAR systems, the CPSAR offer advantages in terms of the larger swath and lower data rate. The mxe2x80xafxe2x88x92xe2x80xafxcfx87 CP decomposition considers two out of the three Stokes child parameters: degree of polarization (m), ellipticity (xcfx87), and orientation angle (xcfx88) to describe the polarized part of the quasimonochromatic partially polarized wave. An improvement in the scattering powers was proposed in the Sxe2x80xafxe2x88x92xe2x80xafxcexa9 decomposition, which takes into accounts both the transmitted and received wave ellipticities (xcfx87t, xcfx87r) and the orientation angles (xcfx88t, xcfx88r). In this decomposition, S denotes the Stokes vector and xcexa9 is the polarized power fraction. However, it may be noted that the Sxe2x80xafxe2x88x92xe2x80xafxcexa9 decomposition intrinsically ignores dominance in the target scattering mechanism while calculating the powers. In this work, improvement is proposed for the Sxe2x80xafxe2x88x92xe2x80xafxcexa9 decomposition by utilizing the degree of dominance in the scattering mechanism. The improved Sxe2x80xafxe2x88x92xe2x80xafxcexa9 (named as iSxe2x80xafxe2x88x92xe2x80xafxcexa9) decomposition powers are first compared with the existing mxe2x80xafxe2x88x92xe2x80xafxcfx87 and Sxe2x80xafxe2x88x92xe2x80xafxcexa9 powers for elementary (viz., trihedral and dihedral corner reflectors) and distributed targets using simulated CPSAR data from quadpol RADARSAT2 data. An increase of xe2x88xbc2% for odd and evenbounce powers obtained from the iSxe2x80xafxe2x88x92xe2x80xafxcexa9 decomposition is observed for the trihedral and dihedral corner reflectors respectively. The analysis of the scattering powers for distributed targets shows that an increase of 15% and 12% in the even and oddbounce powers is observed from iSxe2x80xafxe2x88x92xe2x80xafxcexa9 for urban and bare soil areas respectively as compared to the mxe2x80xafxe2x88x92xe2x80xafxcfx87 and Sxe2x80xafxe2x88x92xe2x80xafxcexa9 decompositions. Besides, temporal variations in the scattering powers obtained from the iSxe2x80xafxe2x88x92xe2x80xafxcexa9 decomposition are also analyzed for rice, cotton, and sugarcane crops at different growth stages.

Low Rank Plus Sparse Decomposition of Synthetic Aperture Radar Data for Target Imaging We analyze synthetic aperture radar (SAR) imaging of complex ground scenes that contain both stationary and moving targets. In the usual SAR acquisition scheme, we consider ways to preprocess the data so as to separate the contributions of the moving targets from those due to stationary background reflectors. Both components of the data, that is, reflections from stationary and moving targets, are considered as signal and are needed for target imaging and tracking, respectively. The approach we use is to decompose the data matrix into a low rank and a sparse part. This decomposition enables us to capture the reflections from moving targets into the sparse part and those from stationary targets into the low rank part of the data. The computational tool for this is robust principal component analysis (RPCA) applied to the SAR data matrix. We also introduce a lossless baseband transformation of the data, which simplifies the analysis and improves the performance of the RPCA algorithm. A modified version of RPCA, the stable principal component pursuit (PCP), is robust to additive noise. Our main contribution is a theoretical analysis that determines an optimal choice of parameters for the RPCA algorithm so as to have an effective and stable separation of SAR data coming from moving and stationary targets. This analysis also gives a lower bound for detectable target velocities. We show in particular that the rank of the sparse matrix is proportional to the square root of the targetu0027s speed in the direction that connects the SAR platform trajectory to the imaging region. The robustness of the approach is illustrated with numerical simulations in the Xband SAR regime.