Application of texture analysis in coastal object classification

Jun Fu; Dongqi Gu; Huiliang Yang

doi:10.1117/12.902009

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23 November 2011 Application of texture analysis in coastal object classification

Jun Fu, Dongqi Gu, Huiliang Yang

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Proceedings Volume 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 800612 (2011) https://doi.org/10.1117/12.902009
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

Texture feature of image is one of the most important factors in the processing of information extraction from satellite scene image. In this paper the texture feature analysis was introduced in the processing of the classification of the objects in coastal zone. During the texture analysis process, how to extract effectively the texture features is the key factor. In the experiment of coastal classification, this paper introduced a method of a set of texture features selection based on step-by-step discriminance. Texture is described by Gray level co-occurrence matrix in this study, and there are 192 texture features to describe the characteristics of coastal objects. With the features selection method presented by this paper, five values were chosen as the representatives to classify the object texture feature. By means of the neural networks the object classification mode based on the texture features was defined and the object classifications of the southern coast of Laizhou Bay were carried out. Results show the step-by-step discriminance not only can decrease the dimension of the texture feature database, but also ensure and improve the accuracy of the classification, and the classification accuracy was up to 83.4%. The neural networks mode is the most effective method to account for the classification of the typical objects in coastal zone.

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Jun Fu, Dongqi Gu, and Huiliang Yang "Application of texture analysis in coastal object classification", Proc. SPIE 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 800612 (23 November 2011); https://doi.org/10.1117/12.902009

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