Land cover classification in mining areas using Beijing-1 small satellite data

Abstract

Land cover classification is conducted using the panchromatic and multi-spectral data of Beijing-1 small satellite data in the western part of Xuzhou coal mining area. Firstly, fusion images obtained from different pixel fusion methods are used to land cover classification using SVM classifier. Secondly, feature level fusion is implemented by extracting texture information from panchromatic data and NDVI from multi-spectral data, by which texture and spectral features form new vectors to SVM classifier. Finally, Decision level fusion is experimented by adopting Dempster-Shafer evidence theory for classifier combination. The experimental results show that the fusion of panchromatic and multi-spectral data of Beijing-1 small satellite is effective to land cover classification, and the decision level fusion algorithm outperforms other methods in terms of classification accuracy.