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28 October 2006 The utility of texture analysis to improve per-pixel classification for CBERS02's CCD image
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Proceedings Volume 6419, Geoinformatics 2006: Remotely Sensed Data and Information; 64191C (2006)
Event: Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 2006, Wuhan, China
The maximum likelihood classification (MLC) is one of the most popular methods in remote sensing image classification. Because the maximum likelihood classification is based on spectrum of objects, it cannot correctly distinguish objects that have same spectrum and cannot reach the accuracy requirement. In this paper, we take an area of Langfang of Hebei province in China as an example and discuss the method of combining texture of panchromatic image with spectrum to improve the accuracy of CBERS02 CCD image information extraction. Firstly, analysis of the textures of the panchromatic image (CCD5) made by using texture analysis of Gray Level Coocurrence Matrices and statistic index. Then optimal texture window size of angular second moment, contrast, entropy and correlation is obtained according to variation coefficient of each texture measure for each thematic class. The chosen optimal window size is that from which the value of variation coefficient starts to stabilize while having the smallest value. The output images generated by texture analysis are used as additional bands together with other multi-spectral bands(CCD1-4) in classification. Objects that have same spectrums can be distinguished. Finally, the accuracy measurement is compared with the classification based on spectrum only .The result indicates that the objects with same spectrum are distinguished by using texture analysis in image classification, and the spectral /textural combination improves more than spectrum only in classification accuracy.
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Guangxiong Peng, Yuhua He, Jing Li, Yunhao Chen, and Deyong Hu "The utility of texture analysis to improve per-pixel classification for CBERS02's CCD image", Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 64191C (28 October 2006);

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