Abstract
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.
Highlights
With the development of remote sensing technology, high spatial resolution remote sensing data have been more acquired and widely applied (Yuan and He, 2008)
In order to improve the accuracy of high spatial resolution remote sensing image classification, additional information should be incorporated into the classification process
This paper proposed a method of object-based land cover classification for high spatial resolution ALOS images combining the spectral information mapped from TM images
Summary
With the development of remote sensing technology, high spatial resolution remote sensing data have been more acquired and widely applied (Yuan and He, 2008). Zhang and Zhu (2011) put forward a knowledge-rule-based classification method for high spatial resolution remote sensing images combining the spectral, texture and shape features together. High spatial resolution remote sensing data often contain two types of images, those with a single panchromatic band and those with four multispectral bands(Wang et al, 2012). The high spatial resolution remote sensing images have less multispectral bands than low or medium spatial resolution remote sensing images. The low or medium spatial resolution remote sensing images contain more spectral information than high spatial resolution remote sensing data. This paper proposed a method of object-based land cover classification for high spatial resolution ALOS images combining the spectral information mapped from TM images.
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