Watershed-Based Attribute Profiles With Semantic Prior Knowledge for Remote Sensing Image Analysis

Abstract

In this article, we develop a novel feature extraction method that combines two well-established mathematical morphology concepts: watersheds and morphological attribute profiles (APs). In order to extract spatial-spectral features from remote sensing data, APs were originally defined as sequences of filtering operators on inclusion trees, i.e. the max-and mintrees, computed from the input image. In this study, we extend the AP paradigm to the more general framework of hierarchical watersheds.Moreover, we explore the semantic knowledge provided by labeled training pixels during different phases of the Watershed-AP construction, namely within the construction of hierarchical watersheds from the raw image and later within the filtering of the resulting hierarchy. We illustrate the relevance of the proposed method with two applications including land cover classification and building extraction using optical remote sensing images. Experimental results show that the new profiles outperform various existing features using two public datasets (Zurich and Vaihingen), thus providing another high potential feature extraction method within the AP family.

Method of Water Body Information Extraction in Complex Geographical Environment from Remote Sensing Images

“…(58,59) Using the internal filtering function, erosion can eliminate the small fragments in an image and shrink the image. (60)(61)(62) Opening can smooth the image, eliminating tiny objects at the edge such as "burrs," or remove the isolated pixels or blocks in an image. Closing has the effect of image filtering, filling up small holes and gaps in an image and filtering nearby objects with a smoothed boundary.…”