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Proceedings Paper

Classification of polarimetric SAR data using dictionary learning
Author(s): Jacob S. Vestergaard; Anders L. Dahl; Rasmus Larsen; Allan A. Nielsen
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Paper Abstract

This contribution deals with classification of multilook fully polarimetric synthetic aperture radar (SAR) data by learning a dictionary of crop types present in the Foulum test site. The Foulum test site contains a large number of agricultural fields, as well as lakes, wooded areas, natural vegetation, grasslands and urban areas, which makes it ideally suited for evaluation of classification algorithms. Dictionary learning centers around building a collection of image patches typical for the classification problem at hand. This requires initial manual labeling of the classes present in the data and is thus a method for supervised classification. The methods aim to maintain a proficient number of typical patches and associated labels. Data is consecutively classified by a nearest neighbor search of the dictionary elements and labeled with probabilities of each class. Each dictionary element consists of one or more features, such as spectral measurements, in a neighborhood around each pixel. For polarimetric SAR data these features are the elements of the complex covariance matrix for each pixel. We quantitatively compare the effect of using different representations of the covariance matrix as the dictionary element features. Furthermore, we compare the method of dictionary learning, in the context of classifying polarimetric SAR data, with standard classification methods based on single-pixel measurements.

Paper Details

Date Published: 8 November 2012
PDF: 9 pages
Proc. SPIE 8537, Image and Signal Processing for Remote Sensing XVIII, 85370X (8 November 2012); doi: 10.1117/12.974814
Show Author Affiliations
Jacob S. Vestergaard, Technical Univ. of Denmark (Denmark)
Anders L. Dahl, Technical Univ. of Denmark (Denmark)
Rasmus Larsen, Technical Univ. of Denmark (Denmark)
Allan A. Nielsen, Technical Univ. of Denmark (Denmark)

Published in SPIE Proceedings Vol. 8537:
Image and Signal Processing for Remote Sensing XVIII
Lorenzo Bruzzone, Editor(s)

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