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Proceedings Paper

Study on the technology of classifying high-resolution remote sensing image based on multi-feature
Author(s): Hui Lin; Jiping Li; Dengkui Mo; Yujiu Xiong; Hua Sun; Xuiying Liu
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Paper Abstract

Image classification is an important technology in the application of remote sensing. Traditional methods of image classification are based on low or medium-resolution images, and the accuracy of classification is always very low. In recent years, high-resolution remote sensing images have significant improvements, but there is still no good method of classification. Studies showed that the accuracy of classified high-resolution images is even lower than that of low or medium -resolution images by traditional classification methods. This turns out that traditional classification technologies appeared to have serious error when using high-resolution images. In this paper, a method of multi-feature classification was introduced to high-resolution remote sensing image, thus avoiding the method of single-feature and pixel-based classification. In this method, pixel-based high-resolution images are changed into object-based images by segmentation. Models of area, perimeter, length, width, symmetry, ratio of length and width, rectangular fit and compactness were established to measure features of segmented objects. More over, the new method of using spectral and texture features to classify high-resolution images was completed. The result showed that the accuracy of image classification can be up to 91.6% by the multi-featured classification, which proved to have improved high-resolution remote sensing image classification.

Paper Details

Date Published: 19 May 2006
PDF: 9 pages
Proc. SPIE 6199, Remote Sensing and Space Technology for Multidisciplinary Research and Applications, 61990H (19 May 2006); doi: 10.1117/12.673666
Show Author Affiliations
Hui Lin, Central South Forestry Univ. (China)
Jiping Li, Central South Forestry Univ. (China)
Dengkui Mo, Central South Forestry Univ. (China)
Yujiu Xiong, Central South Forestry Univ. (China)
Hua Sun, Central South Forestry Univ. (China)
Xuiying Liu, Central South Forestry Univ. (China)

Published in SPIE Proceedings Vol. 6199:
Remote Sensing and Space Technology for Multidisciplinary Research and Applications
Qingxi Tong; Xiuwan Chen; Allen Huang; Wei Gao, Editor(s)

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