期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 11, 期 8, 页码 1409-1413出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2013.2294241
关键词
Extended attribute profile (EAP); graph cut; segmentation; sparse representation; very high resolution (VHR) images
类别
资金
- National Basic Research Program of China (973 Program) [2011CB707105]
- National Natural Science Foundation of China [61201342, 40930532]
- Program for Changjiang Scholars and Innovative Research Team in University [IRT1278]
In this letter, a novel supervised segmentation technique based on sparsely representing the stacked extended morphological attribute profiles (EAPs) and maximum a posteriori probability (MAP) is presented for very high resolution (VHR) images. Attribute profiles (APs), which are extracted by using several attributes, are applied to the multispectral VHR image, leading to a set of extended EAPs. Using the sparse prior of representing the pixel with all training samples, the extended multi-AP (EMAP) feature stacked by the EAP features is transformed into a class-dependent residual feature, which can be normalized as a posterior probability distribution of the pixel. A graph-cut approach is utilized to segment the image scene and obtain the final classification result. Experiments were conducted on IKONOS and WorldView-2 data sets. Compared with SVM, object-oriented SVM with majority voting, and some other state-of-the-art methods, the proposed method shows stable and effective results.
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