Basic Principles of SAR Polarimetry

Abstract

This chapter critically summarizes the main theoretical aspects necessary for a correct processing and interpretation of the polarimetric information towards the development of applications of synthetic aperture radar (SAR) polarimetry. First of all, the basic principles of wave polarimetry (which deals with the representation and the understanding of the polarization state of an electromagnetic wave) and scattering polarimetry (which concerns inferring the properties of a target given the incident and the scattered polarized electromagnetic waves) are given. Then, concepts regarding the description of polarimetric data are reviewed, covering statistical and scattering aspects, the latter in terms of coherent and incoherent decomposition techniques. Finally, polarimetric SAR interferometry and tomography, two acquisition modes that enable the extraction of the 3-D scatterer position and separation, respectively, and their polarimetric characterization, are described.

Application of Multitemporal Change Detection in Radar Satellite Imagery Using REACTIV-Based Method for Geospatial Intelligence

“…This is why vertically polarised waves (VV) interact better with spatial objects (those that stand out above the ground). The change of wave polarisation results from multiple scattering in the layer, and thus the recorded intensity of cross-polarised wave (VH) is lower than that of co-polarised wave (VV) [ 45 ].…”

A Review on PolSAR Decompositions for Feature Extraction

“…Polarimetric target decompositions, a class of algorithms, have been developed to extract diverse features from PolSAR images, enhancing their utility in classification and target detection tasks by capturing multiple aspects. Many of these algorithms have been proposed in the literature [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. These methods can be broadly categorized as either coherent or non-coherent decomposition techniques [18].…”

“…Many of these algorithms have been proposed in the literature [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. These methods can be broadly categorized as either coherent or non-coherent decomposition techniques [18]. Each of these methods tackles the task of interpreting the information contained in PolSAR cells by starting with the underlying assumption about the target.…”

“…As we stand on the cusp of artificial intelligence dominance, the exploration of machine learning techniques [10,11] and, more prominently, deep learning approaches is gaining traction for extracting intricate polarimetric features [12,13]. This emphasis is particularly pronounced in the domain of Polarimetric Synthetic Aperture Radar (Pol-SAR) [14][15][16][17][18].…”

Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification

“…By applying this technique, three main polarimetric parameters can be extracted: entropy (H), anisotropy (A), and mean alpha angle (α). Entropy indicates the degree of statistical disorder of the scattering process, anisotropy is a measure of the relative importance of the secondary scattering mechanisms, providing valuable information in cases of targets with high values of entropy (H > 0.7), and mean alpha angle value represents the dominant mechanism taking place in the scattering phenomenon, being single-bounce scattering, volume scattering, or double-bounce scattering for values close to 0 • , 45 , respectively [39]. Apart from the three above decomposition parameters, several others polarimetric parameters can be obtained by the specific method, using the open source "Polarimetric SAR data Processing and Education Toolbox" (PolSARpro) [40], provided by the European Space Agency (ESA).…”