Region based segmentation of QuickBird multispectral imagery through band ratios and fuzzy comparison

The continued advancements in satellite sensor technologies have increased the number of objects that can be discriminated within satellite imagery. Effective segmentation of high resolution satellite imagery is Currently a hot topic of research. Existing segmentation algorithms and applications contain many parameters and options which require the operator to select a proper set of parameters for a given data set. The setting of these parameters can be quite tedious and the same set of parameters may or may not work from one high resolution satellite image scene to the next. This paper presents a modification of a region based approach for unsupervised segmentation of high resolution satellite imagery as a solution to segmentation of land use coverage in QuickBird multispectral 2.44 m imagery. This type of segmentation is important to a variety of applications such as land use classification and urban planning. All region based segmentation approaches require a method for representing image regions/segments and judging the similarity between two given image regions/segments. In the proposed modification of this paper, region description is provided through the integration of band ratios. Region similarity measures are performed using Fuzzy Logic. The Hierarchical Split Merge Refinement (HSMR) algorithmic framework for unsupervised image segmentation is the foundation for this modification. In addition, this paper improves upon the merging and refinement processes of the HSMR algorithm. Test results demonstrate stable segmentation of land use areas across a variety of high resolution satellite images. (c) 2008 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.