Texture feature extraction for land-cover classification of remote sensing data in land consolidation district using semi-vaniogram

The area of land consolidation projects are generally small, so remote sensing images used in land cover classification are generally of high resolution. The spectral characteristics of the high-resolution remote sensing data are unstable, while the texture feature is prominent. In view of this issue, this paper study the spatial relation between the adjacent pixels in the remote sensing image, and selected the lag distance of the serm-variogram that is determined when the value of the semi-variogram tended to be constant as the co-occurrence window size. Sometimes the window size is the most important influencing factor in the texture feature extraction process. Moreover, under the restraint of the classification results, this paper introduces a method to compute the co-occurrence features with a timely changeable co-occurrence window size according to the semi-variogram analysis. This paper takes Zhaoquanying land consolidation project located at Beijing Shunyi District as an example, the texture feature is extracted from SPOT5 remote sensing data of land consolidation project area in the TitanImage secondary development environment. The results show that the classification accuracy has improved.