Advanced Statistical Modelling of Polarimetric SAR Data for Land Cover Change Detection Analysis

Citation

Akbari V, Bouhlel N & Méric S (2024) Advanced Statistical Modelling of Polarimetric SAR Data for Land Cover Change Detection Analysis. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 07.07.2024-12.07.2024. https://doi.org/10.1109/igarss53475.2024.10642175

Abstract
In this paper, we will present a determinant ratio test (DRT) statistic to measure the similarity of two covariance matrices for unsupervised change detection in polarimetric radar images. The multilook complex covariance matrix is assumed to follow a scaled complex Wishart distribution. In doing so, the distribution of the DRT statistic is analytically derived which is exactly Wilks’s lambda of the second kind distribution, with density expressed in terms of Meijer G-functions. Due to this distribution, the constant false alarm rate (CFAR) algorithm is derived in order to achieve the required performance. More specifically, a threshold is provided by the CFAR to apply to the DRT statistic producing a binary change map. Finally, simulated and real multilook polarimetric radar data are employed to assess the performance of the method and is compared with the Hotelling–Lawley trace (HLT) statistic.