TRANSFORMATION BASED ALGORITHMS FOR CHANGE DETECTION IN FULL POLARIMETRIC REMOTE SENSING IMAGES

Thanks to the recent advances in the development of polarimetric synthetic aperture radar (SAR) sensors, this remote sensing field attracts many applications. Among the different applications of these data, change detection is one of the most important applications. PolSAR images, due to interactions between electromagnetic waves and the target, could be used to study changes in the Earth's surface. This paper is a type of transformation-based method for polarimetric change detection (CD) purpose. For this purpose, we use full polarimetry imaging radar and extracted 138 features based on decomposition. The CD methods are the principal component analysis (PCA), the Multivariate Alteration Detection (MAD), the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD), the Covariance Equalization (CE), and the Cross-Covariance (CRC). Assessment of the incorporated methods performed using most common criteria as quantity and quality assessment, such as overall accuracy (OA), kappa coefficient, and as visual analysis. The results of the experiments show that CC has better performance compared with other algorithms.