A classification method for remotely sensed imagery by integrating with spatial structure information

Yong Ge 1, Hexiang Bai 1,2 and Deyu Li 2
1 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic
Sciences & Natural Resources Research, Chinese Academy of Sciences
Beijing 100101, China.
Tel: +86 10 64888967; Fax: +86 10 64889630
2 School of Computer and Information Technology, Shanxi University
Taiyuan 030006, China
Tel: +86 351 7018775
baihx@lreis.ac.cn; lidy@sxu.edu.cn

Remote Sensing technologies have been widely applied to monitoring natural and man-made phenomena such as desertification, land cover changes, coastal environments and environmental pollutions. Information extraction technologies from remotely sensed imagery as an important tool to understand and analyze nature phenomena on earth have been given great attention over past decades. However, only spectral information is not enough to obtain accurate information of interest in some cases, for instance the spectral values of shadows of clouds and water body can be confused easily when classification in TM imagery. Therefore how to incorporate spatial structure or spatial pattern of surface features to extracting process to improve the reliability of results has been investigated in lots of literatures. In this paper, we propose the application of multiple-point simulation (MPS) to the classification of remotely sensed imagery. In order to illustrate the advantage of integrating spatial structure information into classification process, we compare the results of Maximum Likelihood Classification (MLC) and MLC with spatial structure information from MPS in this paper. The latter gives a superior overall performance in the classification of remotely sensed imagery.

Keywords: remotely sensed imagery, information extraction, multiple-point simulation, MLC

In: Caetano, M. and Painho, M. (eds). Proceedings of the 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, 5 – 7 July 2006, Lisboa, Instituto Geográfico Português

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