Improvement of Sentinel-1 Remote Sensing Data Classification by DWT and PCA

Abstract

This article presents a new alternative for data resource, by applying the proposed methods of Principal Components Analysis (PCA) or of Discrete Wavelet Transformation (DWT) on the VV and VH polarization images of the Sentinel-1 radar satellite, aiming at a better classification of data. The study area concerns the Houareb site located in the city of Kairouan in central Tunisia. In addition to Sentinel-1 data, field truth data and the Euclidian Minimum Distance (EMD) criterion were used for classification and validation. Energy descriptors have been proposed in this study for classifications. Cross validation was used to evaluate the results of the classification. The best classification result was achieved using the DWT method applied on VH and VV images with an Overall Precision (OA) of 0.671 and 0.548, respectively, against an OA value of 0.371 and of 0.449 when the PCA method and the Minimum Distance (MDist) classifier were applied on the dual (VV; VH) polarization, respectively. The DWT transformation gives the highest Kappa Precision Coefficient (KPC) of 0.8.