MUM (Merge Using Moments) segmentation for SAR images
| Autor: | Ian McConnell, Christopher John Oliver, Edward Welbourne, Rod Cook |
|---|---|
| Rok vydání: | 1994 |
| Předmět: |
Synthetic aperture radar
Segmentation-based object categorization business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation Image segmentation Multiplicative noise Speckle pattern Signal-to-noise ratio Geography Computer Science::Computer Vision and Pattern Recognition Segmentation Computer vision Artificial intelligence business |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.197529 |
| Popis: | In Synthetic Aperture Radar (SAR) and other systems employing coherent illumination to form high-resolution images, the resulting image is generally corrupted by a form of multiplicative noise, known as coherent speckle, with a signal-to-noise ration of unity. This severe form of noise presents singular problems for image processing software of all kinds. This paper describes a segmentation scheme, Merge Using Moments (MUM), for image corrupted by coherent speckle. The image is initially massively over-segmented. A scheme based on examination of the statistical properties (moments) of adjoining regions is employed to improve an over-fine segmentation by merging regions to produce a coarser segmentation. This scheme is employed iteratively until no remaining merge appears valid, at which time a good segmentation is obtained. Segmentation using μm on SAR imagery are given and the results compared to other segmentation schemes. The results of using it on typical SAR images illustrate its potential. |
| Databáze: | OpenAIRE |
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