Classification of Polarimetric SAR Data Using Spectral Graph Partitioning

TitleClassification of Polarimetric SAR Data Using Spectral Graph Partitioning
Publication TypeConference Paper
Year of Publication2006
AuthorsErsahin, K., I. G. Cumming, and M. J. Yedlin
Conference NameGeoscience and Remote Sensing Symposium, 2006. IGARSS 2006. IEEE International Conference on
Pagination1756 -1759
Date Publishedjul.
Keywordsautomated analysis, automatic optical inspection, classification performance, computer vision, contour-based segmentation, edge-aligned patch histograms, geophysical signal processing, grouping problem, image classification, image segmentation, optical imagery, optical images, polarimetric SAR data, polarimetric synthetic aperture radar, POLSAR, radar polarimetry, region-based segmentation, spatial proximity, spectral graph partitioning, visual interpretation, Wishart classifier

A new approach for classification of Polarimetric Synthetic Aperture Radar (POLSAR) data is proposed using segmentation that is formulated as a graph partitioning problem. This work is motivated by the fact that human experts are very good at visual interpretation and segmentation of POLSAR data, which is often challenging for automated analysis techniques. Spectral graph partitioning, a framework that has recently emerged in computer vision for solving grouping problems with perceptually plausible results, is used with modifications necessary to accommodate POLSAR data. Using the similarity of edge- aligned patch histograms and spatial proximity, classification performance that is superior to the Wishart classifier is achieved. This approach also provides a way to combine region-based and contour-based segmentation techniques, as it can accommodate different representations of polarimetric data as well as other data sources (e.g., optical imagery).


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