Fusion and classification of Beijing-1 small satellite remote sensing image for land cover monitoring in mining area

Abstract

In order to promote the application of Beijing-1 small satellite (BJ-1) remote sensing data, the multispectral and panchromatic images captured by BJ-1 were used for land cover classification in Pangzhuang Coal Mining. An improved Intensity-Hue-Saturation (IHS) fusion algorithm is proposed to fuse panchromatic and multispectral images, in which intensity component and panchromatic image are combined using the weights determined by edge pixels in the panchromatic image identified by grey absolute correlation degree. This improved IHS fusion algorithm outperforms traditional IHS fusion method to a certain extent, evidenced by its ability in preserving spectral information and enhancing spatial details. Dempster-Shafer (D-S) evidence theory was adopted to combine the outputs of three member classifiers to generate the final classification map with higher accuracy than that by any individual classifier. Based on this study, we conclude that Beijing-1 small satellite remote sensing images are useful to monitor and analyze land cover change and ecological environment degradation in mining areas, and the proposed fusion algorithms at data and decision levels can integrate the advantages of multi-resolution images and multiple classifiers, improve the overall accuracy and produce a more reliable land cover map.