Experimental study on multi-sub-classifier for land cover classification: a case study in Shangri-La, China

Yan-ying Wang; Jin-liang Wang; Ping Wang; Wen-yin Hu; Shao-hua Su

doi:10.1117/12.2208247

9 December 2015 Experimental study on multi-sub-classifier for land cover classification: a case study in Shangri-La, China

Yan-ying Wang, Jin-liang Wang, Ping Wang, Wen-yin Hu, Shao-hua Su

Author Affiliations +

Proceedings Volume 9808, International Conference on Intelligent Earth Observing and Applications 2015; 98082O (2015) https://doi.org/10.1117/12.2208247
Event: International Conference on Intelligent Earth Observing and Applications, 2015, Guilin, China

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.

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Yan-ying Wang, Jin-liang Wang, Ping Wang, Wen-yin Hu, and Shao-hua Su "Experimental study on multi-sub-classifier for land cover classification: a case study in Shangri-La, China", Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98082O (9 December 2015); https://doi.org/10.1117/12.2208247

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KEYWORDS

Image classification

Remote sensing

Roads

Earth observing sensors

Landsat

Pattern recognition

Artificial neural networks

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