Segmentation of polarimetric SAR data using contour information via spectral graph partitioning

A new method for segmenting polarimetric Synthetic Aperture Radar (POLSAR) data is proposed. Image segmentation is formulated as a graph partitioning problem. Spectral graph partitioning - known to provide perceptually plausible image segmentation results using one or more cues (e.g., similarity, pr...

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Bibliographic Details
Published in2007 IEEE International Geoscience and Remote Sensing Symposium pp. 2240 - 2243
Main Authors Ersahin, K., Cumming, I.G., Ward, R.K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2007
Subjects
Clustering methods
Computer vision
Cost function
Humans
Image segmentation
Pixel
Polarimetric synthetic aperture radar
Psychology
Synthetic aperture radar
Testing
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Summary:A new method for segmenting polarimetric Synthetic Aperture Radar (POLSAR) data is proposed. Image segmentation is formulated as a graph partitioning problem. Spectral graph partitioning - known to provide perceptually plausible image segmentation results using one or more cues (e.g., similarity, proximity, contour continuity) - is applied on POLSAR image data. The degree of similarities between pairs of pixels are calculated based on contour information. Graph partitioning is performed using the Multiclass Spectral Clustering method that minimizes the normalized cut cost function to ensure minimal similarity between partitions. The resulting segmentation is an approximation to the global optimal solution. C-band POLSAR data acquired by CV-580 are used for testing the performance. The results are found to closely agree with manual segmentations.
ISBN:9781424412112
1424412110
ISSN:2153-6996
2153-7003
DOI:10.1109/IGARSS.2007.4423285