Object-Based Land Cover Classification for ALOS Image Combining TM Spectral

Abstract. Land cover classification for high spatial resolution remote sensing images becomes a challenging work. The high spatial resolution remote sensing images have more spatial information. The low or medium resolution remote sensing images have more spectral information. In order to improve the accuracy of high spatial resolution remote sensing image classification, additional information should be incorporated into the classification process of high spatial resolution remote sensing image. This paper proposed a method of object-based land cover classification for high spatial resolution ALOS images combining the spectral information of TM images. First, the high spatial resolution ALOS panchromatic image was segmented by multi-resolution segmentation method. Second, the spectral features of segmented regions were extracted from multi-spectral ALOS image and TM image by spatial mapping mechanism. Third, the regions were classified by SVM classifier. Experimental results show that the classification method for high spatial resolution remote sensing images combining the TM spectral information based on the spatial mapping mechanism can make use of the spectral information both in high and low spatial resolution remote sensing images and improve classification accuracy.