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Attribute Learning for SAR Image Classification


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Electronic and Information School, Wuhan University, Wuhan 430072, China





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Abstract 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





Autor: Chu He * , Xinlong Liu, Chenyao Kang, Dong Chen and Mingsheng Liao

Fuente: http://mdpi.com/



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