Classification of cloudy hyperspectral image and LiDAR data based on feature fusion and decision fusion
Hyperspectral and LiDAR data, can provide plentiful information about the objects on the Earths surface. However there are some shortages for each of them, where hyperspectral sensor is easily influenced by cloud and difficult to distinguish different objects contained same materials, LiDAR cannot discriminate different objects which are similar in altitude. Fusion of these multi-source data for reliable classification attracts increasing interests but remains challenging. In this paper, we propose a new framework to fuse multi-source data for classification. The proposed method contains three main works: 1) cloud shadows extraction; 2) feature fusion of spectral and spatial information extracted from hyperspectral image, elevation information extracted from LiDAR data; 3) decision fusion of cloud and non-cloud regions. Experimental results on real HSI and LiDAR data demonstrate effectiveness of the proposed method both visually and quantitatively.