Application of Polarimetric-SAR Decompositions on RADARSAT-2 Fine Quad-Pol Images to Enhance the Performances of Ships Detection Algorithms

Mahgoun, Hichem; Chaffa, Nour Elhouda; Ouarzeddine, Mounira; Souissi, Boularbah

doi:10.1007/s11220-020-00321-3

Cited by 8 publications

(1 citation statement)

References 16 publications

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“…Furthermore, Fan. W. et al [41] utilized Pauli decomposition in order to generate color-coded images and feed Deep Convolutional Neural Networks in a ship detection task, while in [42], Pauli's, along with three other decomposition approaches, were employed in a ship detection study that combines algorithms aiming to improve the probability of target detection.…”

Section: Pauli Target Decompositionmentioning

confidence: 99%

A Review on PolSAR Decompositions for Feature Extraction

Karachristos,

Koukiou,

Anastassopoulos

2024

J. Imaging

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Feature extraction plays a pivotal role in processing remote sensing datasets, especially in the realm of fully polarimetric data. This review investigates a variety of polarimetric decomposition techniques aimed at extracting comprehensive information from polarimetric imagery. These techniques are categorized as coherent and non-coherent methods, depending on their assumptions about the distribution of information among polarimetric cells. The review explores well-established and innovative approaches in polarimetric decomposition within both categories. It begins with a thorough examination of the foundational Pauli decomposition, a key algorithm in this field. Within the coherent category, the Cameron target decomposition is extensively explored, shedding light on its underlying principles. Transitioning to the non-coherent domain, the review investigates the Freeman–Durden decomposition and its extension, the Yamaguchi’s approach. Additionally, the widely recognized eigenvector–eigenvalue decomposition introduced by Cloude and Pottier is scrutinized. Furthermore, each method undergoes experimental testing on the benchmark dataset of the broader Vancouver area, offering a robust analysis of their efficacy. The primary objective of this review is to systematically present well-established polarimetric decomposition algorithms, elucidating the underlying mathematical foundations of each. The aim is to facilitate a profound understanding of these approaches, coupled with insights into potential combinations for diverse applications.

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Section: Pauli Target Decompositionmentioning

confidence: 99%