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.

中文翻译：

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DWT和PCA改进Sentinel-1遥感数据分类

本文通过对Sentinel-1雷达卫星的VV和VH极化图像应用主成分分析（PCA）或离散小波变换（DWT）的方法，提出了一种新的数据资源替代方法，目的是进行更好的分类数据的。研究区域涉及位于突尼斯中部凯鲁万市的Houareb基地。除了Sentinel-1数据之外，还使用场真数据和欧几里德最小距离（EMD）标准进行分类和验证。在这项研究中已经提出了能量描述符用于分类。交叉验证用于评估分类结果。使用DWT方法在VH和VV图像上分别以0.671和0.548的总精度（OA）相对于OA值0可获得最佳分类结果。当PCA方法和最小距离（MDist）分类器分别应用于双重（VV; VH）极化时，分别为371和0.449。DWT转换给出的最高Kappa精度系数（KPC）为0.8。