Land-cover classification using ASTER multi-band combinations based on wavelet fusion and SOM neural network

摘要

We developed a land-cover classification methodology using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible near-infrared (VNIR), shortwave infrared (SWIR), and thermal infrared (TIR) band combinations based on wavelet fusion and the self-organizing map (SOM) neural network methods, and compared the classification accuracies of different combinations of ASTER multi-band data. The study area is located in the Hetao Irrigation District, Inner Mongolia Autonomous Region, China. This area is located in the middle of dry grassland on the edge of the Gobi Desert in Inner Mongolia, agriculture is impossible without irrigation. Field surveys were conducted several times in 2000, 2002 and 2003. A wavelet fusion concept named ARSIS (Amelioration de la Resolution Spatiale par Injection de Structures) was used to fuse ASTER data in the preprocessing stage. In order to apply the wavelet fusion method to ASTER data, the principal components of ASTER VNIR data were computed. The first principal component was used as the base image for wavelet fusion. In our experiments, the spatial resolution of ASTER VNIR, SWIR, and TIR data was adjusted to the same 15 m. SOM classification accuracy was increased from 83 to 93% by this fusion, and classification accuracy increased along with the increase of band numbers. Classification accuracy reaches the highest value when all 14 bands are used, but classification accuracy closely approached the highest value when three VNIR bands, three SWIR bands, and two TIR bands were used. A similar tendency was also obtained by the maximum likelihood classification (MLC) method, but the classification accuracies of MLC over all band combinations were significantly lower than those obtained by the SOM method.