Automatic Segmentation of High-resolution Satellite Imagery by Integrating Texture, Intensity, and Color Features

Abstract

High spatial resolution satellite imagery has become an important source of information for geospatial applications. Automatic segmentation of high-resolution satellite imagery is useful for obtaining more timely and accurate information. In this paper, we develop a method and algorithmic framework for automatically segmenting imagery into different regions corresponding to various features of texture, intensity, and color. The central rationale of the method is that information from the three feature channels are adaptively estimated and integrated into a split-merge plus pixel-wise refinement framework. In the procedure for split-merge and refinement, segmentation is realized by comparing similarities between different features of sub-regions. The similarity measure is based on feature distributions. Without a priori knowledge of image content, the image can be segmented into different regions that frequently correspond to different land-use or other objects. Experimental results indicate that the method performs much better in terms of correctness and adaptation than using single feature or multiple features, but with constant weight for each feature. The method can potentially be applied within a broad range of image segmentation contexts.