Paper
17 December 1996 Unsupervised optimal fuzzy clustering and Markov segmentation of polarimetric imaging
Safwan El Assad, Ali Saad, Dominique Barba
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Abstract
This paper presents a method for unsupervised segmentation of polarimetric SAR data into classes of homogeneous microwave backscatter characteristics. Clustering of polarimetric backscatter are obtained either by the CMF-NSO or be SEM algorithm. These algorithms carry out the classification without a priori assumptions on the number of classes in the data set. Assessment of cluster validity is based on performance measures using hypervolume V or CS function criteria. The later measures the overall average compactness and separation of a fuzzy-partition. The CMF-NSO algorithm performs well in situations of large variability of cluster shapes and densities. Given the clusters of polarimetric backscatter, the entire image is segmented using a MAP estimation. Implementation of the MAP technique is accomplished by an ICM algorithm. Results, using fully polarimetric SAR forest data, obtained by the CMF-NSO following by the ICM algorithm with a K-distribution model are quite satisfactory.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Safwan El Assad, Ali Saad, and Dominique Barba "Unsupervised optimal fuzzy clustering and Markov segmentation of polarimetric imaging", Proc. SPIE 2958, Microwave Sensing and Synthetic Aperture Radar, (17 December 1996); https://doi.org/10.1117/12.262691
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KEYWORDS
Polarimetry

Image segmentation

Data modeling

Detection and tracking algorithms

Fuzzy logic

Synthetic aperture radar

Backscatter

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