Supervised Segmentation of Very High Resolution Images by the Use of Extended Morphological Attribute Profiles and a Sparse Transform

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.