Assessing the use of cross-orbit Sentinel-1 images in land cover classification

Abstract

ABSTRACT Land cover is the easiest detectable indicator of human intervention on land. Urban, peri-urban and agriculture areas present a complex combination of land cover, which makes classification challenging. Getting more detailed information is the aim of any classification method. In this study, improving land cover type classification using cross-orbit Sentinel-1 images is evaluated. To avoid uncertainties, three sites in different weather conditions and locations but approximately the same land cover is selected. For each study area, three datasets including polarimetric features extracted from (1) ascending orbit image, (2) descending orbit image, and (3) combined ascending and descending orbit images are produced and used for classification. Land cover classification is performed following a supervised Support Vector Machine (SVM) exploiting all three datasets. Consequently, the radar cross-orbit integrated dataset produced the most accurate land cover map. Classifications show overall accuracies (OA) of 84%, 85%, and 75%, and Kappa coefficients (K) of 0.67, 0.75, and 0.55 for Skane, Tehran, and Sherbrooke regions, respectively. Fortunately, the accuracy results are at least 4% better than single-orbit classification. In other words, the proposed mapping approach has proved that using information from both the ascending and descending dual-polarized images could achieve a more accurate classification map than using a single-orbit image individually.