Cropland Extraction from Very High Spatial Resolution Satellite Imagery by Object-Based Classification Using Improved Mean Shift and One-Class Support Vector Machines
The issue of cropland extraction from very high spatial resolution (VHR) satellite imagery remains a great challenge. In this paper, an object-based classification method for cropland extraction from VHR satellite imagery is proposed based on the improved mean shift and one-class SVM. After the fused satellite image is transformed by nonnegative matrix factorization into three bands, the improved mean shift is employed to segment the image. Subsequently, the structure lines of each region in the segmented image are detected, and the standard deviations of the directions of the straight lines are calculated. The spectral information and the above derived texture information are selected as features for the following classification. At last, the support vector data description is utilized to recognize the croplands from the segmented image based on only some cropland samples. Three satellite images with different spatial resolutions are employed to test the algorithm, and the results show that our proposed method obtains a higher overall classification accuracy than the eCognitions method does, and its overall classification accuracy is promoted with the increasing of spatial resolution. Another merit of our method is that it needs only the cropland samples, which is time-saving and costsaving.
Cropland Extraction Very High Spatial Resolution Object-Based Classification Mean Shift SVM
Jing Shen Jiping Liu Xiangguo Lin Rong Zhao Shenghua Xu
Research Center of Government Geographic Information System, Chinese Academy of Surveying and Mappin Key Laboratory of Mapping from Space of State Bureau of Surveying and Mapping,Chinese Academy of Sur
国际会议
南昌
英文
997-1005
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）