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On the Use of a Shape Constraint in a Pixel-Based SAR Segmentation Algorithm.
- Source :
-
IEEE Transactions on Geoscience & Remote Sensing . Aug2012, Vol. 50 Issue 8, p3158-3170. 13p. - Publication Year :
- 2012
-
Abstract
- A variety of competing algorithms exist for segmentation of both single-channel and multichannel synthetic aperture radar (SAR) images. Among the most successful of these algorithms is the approach presented by Stewart This algorithm defines a cost which is a weighted sum of a likelihood term that estimates the statistical likelihood of the membership of pixels to neighboring segments and a shape term that is intended to provide a smoothing constraint on segment boundaries. The shape term in the original implementation of the Stewart algorithm was rather rudimentary, and in this paper, we explore the performance of a shape term based on Sethian–Osher curvature-flow theory. We demonstrate the performance of the refined curvature-cost (CC) shape term on a set of simulated images as well as an ASAR scene. We assess the segmentation performance using a hybridized shape metric and on the number of segments produced. We find that the CC shape term significantly improves the performance of the Stewart segmentation algorithm, particularly for high-contrast edges. In spite of this success, we argue that further improvements to the algorithm will be difficult due to the architecture of the system. [ABSTRACT FROM PUBLISHER]
- Subjects :
- *SYNTHETIC aperture radar
*ALGORITHMS
*PIXELS
*LEVEL set methods
*COHERENT radar
Subjects
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 50
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 79466260
- Full Text :
- https://doi.org/10.1109/TGRS.2011.2177988