Segmentation of remotely-sensed images by a split-and-merge process+

Abstract

Abstract This paper describes the application of an image segmentation technique to remotely-sensed terrain images used for environmental monitoring. The segmentation is a preprocessing operation which is applied prior to image classification in order to improve classification accuracy from that achievable by classifying pixels individually on the basis of their spectral signatures. The method uses a split-and-merge technique to segment images into regions of homogeneous tone and texture wherever this is possible. The split-and-merge technique employs a hierarchical quadtree data structure. Texture is measured using easily computed grey value difference statistics. The homogeneity criteria employed in region merging are dependent on local statistics. The segmented image is classified using a region classifier for regions and the normal per-pixel classifier for single pixels in areas of inhomogeneity. The technique is illustrated by example classifications of aerial Multispectral Scanner data from two test sites. A quantitative analysis of the performance shows that an increased classification accuracy is achieved.