Next Article in Journal
Camera Coverage Estimation Based on Multistage Grid Subdivision
Previous Article in Journal
Towards Understanding Location Privacy Awareness on Geo-Social Networks
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2017, 6(4), 111;

Attribute Learning for SAR Image Classification

Electronic and Information School, Wuhan University, Wuhan 430072, China
Author to whom correspondence should be addressed.
Received: 17 January 2017 / Revised: 29 March 2017 / Accepted: 1 April 2017 / Published: 5 April 2017
Full-Text   |   PDF [2159 KB, uploaded 7 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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

He, C.; Liu, X.; Kang, C.; Chen, D.; Liao, M. Attribute Learning for SAR Image Classification. ISPRS Int. J. Geo-Inf. 2017, 6, 111.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top