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Attribute Learning for SAR Image Classification

Electronic and Information School, Wuhan University, Wuhan 430072, China
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(4), 111;
Received: 17 January 2017 / Revised: 29 March 2017 / Accepted: 1 April 2017 / Published: 5 April 2017
This paper presents a classification approach based on attribute learning for high spatial resolution Synthetic Aperture Radar (SAR) images. To explore the representative and discriminative attributes of SAR images, first, an iterative unsupervised algorithm is designed to cluster in the low-level feature space, where the maximum edge response and the ratio of mean-to-variance are included; a cross-validation step is applied to prevent overfitting. Second, the most discriminative clustering centers are sorted out to construct an attribute dictionary. By resorting to the attribute dictionary, a representation vector describing certain categories in the SAR image can be generated, which in turn is used to perform the classifying task. The experiments conducted on TerraSAR-X images indicate that those learned attributes have strong visual semantics, which are characterized by bright and dark spots, stripes, or their combinations. The classification method based on these learned attributes achieves better results. View Full-Text
Keywords: Synthetic Aperture Radar (SAR); attribute learning; discriminative clustering; mid-level feature Synthetic Aperture Radar (SAR); attribute learning; discriminative clustering; mid-level feature
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He, C.; Liu, X.; Kang, C.; Chen, D.; Liao, M. Attribute Learning for SAR Image Classification. ISPRS Int. J. Geo-Inf. 2017, 6, 111.

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