Research on methods of building area extraction from high resolution SAR image based on manifold learning
CUI Shiai1,2, CHENG Bo1, LIU Yueming1,2
1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China; 2. University of the Chinese Academy of Sciences, Beijing 100094, China
Abstract：The characteristics of high resolution SAR image is nonlinear and of high dimension. The description of SAR image in which a low dimensional manifold is embedded in high dimensional space is more useful for targets recognition. Therefore, a novel scheme of high resolution SAR image building area extraction is proposed by applying manifold learning to feature representation of a high dimensional SAR targets recognition. Firstly, the high resolution SAR image was preprocessed, and then eight texture features were extracted with gray level co-occurrence matrix (GLCM)so as to construct feature set with gray feature. Adaptive neighborhood selection neighborhood preserving embedding (ANSNPE)algorithm was used to extract the new features from the feature set. Finally, the building area was extracted by threshold segmentation with the new features and post processing, and the accuracy was evaluated. Selecting TerraSAR-X as test data, the authors carried out the experiments. The results show that ANSNPE algorithm can effectively extract the building area from high resolution SAR image, and has strong generalization capability. The projection matrix obtained through the training data can be directly applied to the new samples, and the accuracy of building area extraction could reach higher than 85%.
崔师爱, 程博, 刘岳明. 基于流形学习的高分SAR图像建筑区提取方法[J]. 国土资源遥感, 2017, 29(4): 48-56.
CUI Shiai, CHENG Bo, LIU Yueming. Research on methods of building area extraction from high resolution SAR image based on manifold learning. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 48-56.
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2017, Vol. 29 
