Fine Classification Comparsion of GF-1 GF-5 and Landsat-8 Remote Sensing Data Based on Optimized Sample Selection Method

This paper aims to compare the performance of GaoFen-1 (GF-1), GaoFen-5 (GF-5), Landsat-8 data in fine classification. An optimized sample selection method (OSSM) is developed to ensure the high quality of samples. This method adopts different band combination strategies to realize optimal selection of training samples under the aid of normalized vegetation index (NDVI), normalized water index (NDWI) and the components of Kauth-Thomas (KT) Transformation. After that, support vector machine (SVM) is implemented on these three types of data. Experimental results on China Dunhuang calibration field, Gansu Ying-mao-tuo exploration area and Gan River lower reaches datasets show that GF-5 data performs best in both qualitative and quantitative evaluation of fine classification thanks to its hyperspectral properties.