Integration of SAR Polarimetric Features and Multi-spectral Data for Object-Based Land Cover Classification

Abstract

An object-based approach is proposed for land cover classification using optimal polarimetric parameters. The ability to identify targets is effectively enhanced by the integration of SAR and optical images. The innovation of the presented method can be summarized in the following two main points: ①estimating polarimetric parameters (H-A-Alpha decomposition) through the optical image as a driver; ②a multi-resolution segmentation based on the optical image only is deployed to refine classification results. The proposed method is verified by using Sentinel-1/2 datasets over the Bakersfield area, California. The results are compared against those from pixel-based SVM classification using the ground truth from the National Land Cover Database (NLCD). A detailed accuracy assessment complied with seven classes shows that the proposed method outperforms the conventional approach by around 10%, with an overall accuracy of 92.6% over regions with rich texture.

Keywords

• |synthetic aperture radar (sar)|polarimetric|multispectral|data fusion|object-based|land cover classification