Remote sensing image classification based on geostatistics and ANN

Fengjie Yang; Xiaotao Li; Guangzhu Zhou; Cuiyu Song; Xiaoning Song

doi:10.1117/12.693664

22 December 2006 Remote sensing image classification based on geostatistics and ANN

Fengjie Yang, Xiaotao Li, Guangzhu Zhou, Cuiyu Song, Xiaoning Song

Proceedings Volume 6405, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications; 64052C (2006) https://doi.org/10.1117/12.693664
Event: SPIE Asia-Pacific Remote Sensing, 2006, Goa, India

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.

Citation Download Citation

Fengjie Yang, Xiaotao Li, Guangzhu Zhou, Cuiyu Song, and Xiaoning Song "Remote sensing image classification based on geostatistics and ANN", Proc. SPIE 6405, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications, 64052C (22 December 2006); https://doi.org/10.1117/12.693664

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