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Proceedings Paper

Experimental study on multi-sub-classifier for land cover classification: a case study in Shangri-La, China
Author(s): Yan-ying Wang; Jin-liang Wang; Ping Wang; Wen-yin Hu; Shao-hua Su
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Paper Abstract

High accuracy remote sensed image classification technology is a long-term and continuous pursuit goal of remote sensing applications. In order to evaluate single classification algorithm accuracy, take Landsat TM image as data source, Northwest Yunnan as study area, seven types of land cover classification like Maximum Likelihood Classification has been tested, the results show that: (1)the overall classification accuracy of Maximum Likelihood Classification(MLC), Artificial Neural Network Classification(ANN), Minimum Distance Classification(MinDC) is higher, which is 82.81% and 82.26% and 66.41% respectively; the overall classification accuracy of Parallel Hexahedron Classification(Para), Spectral Information Divergence Classification(SID), Spectral Angle Classification(SAM) is low, which is 37.29%, 38.37, 53.73%, respectively. (2) from each category classification accuracy: although the overall accuracy of the Para is the lowest, it is much higher on grasslands, wetlands, forests, airport land, which is 89.59%, 94.14%, and 89.04%, respectively; the SAM, SID are good at forests classification with higher overall classification accuracy, which is 89.8% and 87.98%, respectively. Although the overall classification accuracy of ANN is very high, the classification accuracy of road, rural residential land and airport land is very low, which is 10.59%, 11% and 11.59% respectively. Other classification methods have their advantages and disadvantages. These results show that, under the same conditions, the same images with different classification methods to classify, there will be a classifier to some features has higher classification accuracy, a classifier to other objects has high classification accuracy, and therefore, we may select multi sub-classifier integration to improve the classification accuracy.

Paper Details

Date Published: 9 December 2015
PDF: 9 pages
Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98082O (9 December 2015); doi: 10.1117/12.2208247
Show Author Affiliations
Yan-ying Wang, Yunnan Normal Univ. (China)
Yunnan Industrial Bureau for National Defense Science and Technology (China)
Jin-liang Wang, Yunnan Normal Univ. (China)
Ping Wang, Yunnan Normal Univ. (China)
Wen-yin Hu, Yunnan Normal Univ. (China)
Shao-hua Su, Yunnan Normal Univ. (China)

Published in SPIE Proceedings Vol. 9808:
International Conference on Intelligent Earth Observing and Applications 2015
Guoqing Zhou; Chuanli Kang, Editor(s)

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