Integrating multiple cues into adaptive hierarchical segmentation for high-resolution remote-sensing images

Abstract

Image segmentation has been, and still is, a hot research topic in computer vision and pattern recognition. However, few existing segmentation algorithms are suitable for all objects presented in high-resolution remote-sensing (HRRS) images, because the relevant methods often implement segmentation in the same mode for the whole image rather than considering the different characteristics of various objects. Therefore, this article proposes an adaptive hierarchical segmentation framework for HRRS images by integrating multiple cues (e.g. intensity, texture and boundary). This two-stage framework first analyses the class of region presented in the study image, then according to this analysis, partitions each region class by adaptively utilizing the proper segmentation method with the most representative features. The distinctive characteristics of this framework are that the first stage simplifies the problem before using the segmentation method, and the second stage guarantees that the segmentation is carried out with the representative cues and corresponding suitable method for these cues. The performance of the proposed segmentation framework is demonstrated through a complete set of experimental results and substantiated using quantitative criteria.