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Proceedings Paper

Remote sensing image classification based on geostatistics and ANN
Author(s): Fengjie Yang; Xiaotao Li; Guangzhu Zhou; Cuiyu Song; Xiaoning Song
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Paper Abstract

Texture is the key character of remote sensing image classification and a lot of studies on this have been done. This article analyzes the current study situation of remote sensing image classification methods and extracting textural information. Moreover, it analyzes the theory of geostatistics. Based on the geostatistics theory, the variogram is applied to extracting textural information of remote sensing image in this article. It has been proven that the textural information can be used to classification by means of test. At the same time, this article discusses the size of computation window, computation direction and step according to the practical application and puts forward to an auto-adaptive method to determine the size of computation window. In addition, it advances a new method to compute textural information, weighted variogram. Considering that the neural network classification has no limitation to data, this study adopts the back propagation neural network method to classify and recognize the matter combining the textural information extracted by variogram and spectral information. Then the classification results are compared with those gained by maximum likelihood method. The analysis result shows that this method can improve the classification precision.

Paper Details

Date Published: 22 December 2006
PDF: 12 pages
Proc. SPIE 6405, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications, 64052C (22 December 2006); doi: 10.1117/12.693664
Show Author Affiliations
Fengjie Yang, Shandong Univ. of Science and Technology (China)
Xiaotao Li, China Institute of Water Resources and Hydropower (China)
Guangzhu Zhou, Shandong Univ. of Science and Technology (China)
Cuiyu Song, Shandong Univ. of Science and Technology (China)
Xiaoning Song, Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 6405:
Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications
William L. Smith; Allen M. Larar; Tadao Aoki; Ram Rattan, Editor(s)

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