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Extraction of enclosure culture area from SPOT-5 image based on texture feature

The east Taihu lake region is characterized by high-density and large areas of enclosure culture area which tend to cause eutrophication of the lake and worsen the quality of its water. This paper takes an area (380×380) of the east Taihu Lake from image as an example and discusses the extraction method of combing texture feature of high resolution image with spectrum information. Firstly, we choose the best combination bands of 1, 3, 4 according to the principles of the maximal entropy combination and OIF index. After applying algorithm of different bands and principal component analysis (PCA) transformation, we realize dimensional reduction and data compression. Subsequently, textures of the first principal component image are analyzed using Gray Level Co-occurrence Matrices (GLCM) getting statistic Eigen values of contrast, entropy and mean. The mean Eigen value is fixed as an optimal index and a appropriate conditional thresholds of extraction are determined. Finally, decision trees are established realizing the extraction of enclosure culture area. Combining the spectrum information with the spatial texture feature, we obtain a satisfied extracted result and provide a technical reference for a wide-spread survey of the enclosure culture area.