The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-7/W2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W2, 263–266, 2013
https://doi.org/10.5194/isprsarchives-XL-7-W2-263-2013
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W2, 263–266, 2013
https://doi.org/10.5194/isprsarchives-XL-7-W2-263-2013

  29 Oct 2013

29 Oct 2013

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

G. Wang1,2, J. Liu1, and G. He1 G. Wang et al.
  • 1Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China
  • 2University of Chinese Academy of Sciences, Beijing, 100049, China

Keywords: Object-based classification, High spatial resolution remote sensing image, Spatial mapping mechanism

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.