17 November 1995 Cooperation of mathematical morphology and region growing for remote sensing image segmentation
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Abstract
Image segmentation consists of partitioning an image into meaningful regions. Two classical approaches are usually employed: the edge-based methods looking for the local grey level discontinuities in the image and the region-based ones seeking parts of the image which are homogeneous in some measurable property such as grey levels, contrast or texture. In the context of high resolution satellite image segmentation, it seems more and more difficult to obtain a faithful automatically segmented image using only one of these approaches. Due to the high complexity of contents of remote sensing images, the current tendency consists in the cooperation of both techniques to alleviate the problems related to each of them taken separately. This paper describes a hierarchical region-based image segmentation scheme combining several powerful tools derived from the mathematical morphology theory and a region growing process. The morphological watershed transformation gives access into the image to highly homogeneous grey level regions producing unfortunately a typical severe oversegmentation. A region-region linkage type growing process is then employed to improve the over-fine segmentation by merging adjacent regions. Two different approaches are employed to measure the similarity between regions. This algorithm has been applied successfully to different types of remote sensing imagery and to a variety of landscapes. All these results show the potentialities offered by the mathematical morphology tool in the field of remote sensing.
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Benoit Ogor, Benoit Ogor, Veronique Haese-Coat, Veronique Haese-Coat, Kidiyo Kpalma, Kidiyo Kpalma, } "Cooperation of mathematical morphology and region growing for remote sensing image segmentation", Proc. SPIE 2579, Image and Signal Processing for Remote Sensing II, (17 November 1995); doi: 10.1117/12.226855; https://doi.org/10.1117/12.226855
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