Texture feature Extraction for Land-cover Classification of Remote Sensing Data in Land Consolidation District Using Semi-variogram analysis

The areas of the land consolidation projects are generally small, so the remote sensing images used in land-cover classification for the land consolidation are generally high spatial resolution images. The spectral complexity of land consolidation objects results in specific limitation using pixel-based analysis for land cover classification such as farmland, woodland, and water. Considering this problem, two approaches are compared in this study. One is the fixed window size co-occurrence texture extraction, and another is the changeable window size according to the result of semi-variogram analysis. Moreover, the methodology for optimizing the co-occurrence window size in terms of classification accuracy performance is introduced in this study. Zhaoquanying land consolidation project is selected as an example, which located in Shunyi District, Beijing, China; texture feature is extracted from SPOT5 remote sensing data in the TitanImage development environment and involved in classification. Accuracy assessment result shows that the classification accuracy has been improved effectively using the method introduced in this paper. Key-Words: Semi-variogram, Land Consolidation, Texture feature, Classification