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Remote Sens. 2015, 7(4), 4157-4177; doi:10.3390/rs70404157

Improved POLSAR Image Classification by the Use of Multi-Feature Combination

1
College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
2
Department of Geography, University of South Carolina, Columbia, SC 29208, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Received: 29 December 2014 / Revised: 26 March 2015 / Accepted: 1 April 2015 / Published: 8 April 2015
View Full-Text   |   Download PDF [16719 KB, uploaded 8 April 2015]   |  

Abstract

Polarimetric SAR (POLSAR) provides a rich set of information about objects on land surfaces. However, not all information works on land surface classification. This study proposes a new, integrated algorithm for optimal urban classification using POLSAR data. Both polarimetric decomposition and time-frequency (TF) decomposition were used to mine the hidden information of objects in POLSAR data, which was then applied in the C5.0 decision tree algorithm for optimal feature selection and classification. Using a NASA/JPL AIRSAR POLSAR scene as an example, the overall accuracy and kappa coefficient of the proposed method reached 91.17% and 0.90 in the L-band, much higher than those achieved by the commonly applied Wishart supervised classification that were 45.65% and 0.41. Meantime, the overall accuracy of the proposed method performed well in both C- and P-bands. Polarimetric decomposition and TF decomposition all proved useful in the process. TF information played a great role in delineation between urban/built-up areas and vegetation. Three polarimetric features (entropy, Shannon entropy, T11 Coherency Matrix element) and one TF feature (HH intensity of coherence) were found most helpful in urban areas classification. This study indicates that the integrated use of polarimetric decomposition and TF decomposition of POLSAR data may provide improved feature extraction in heterogeneous urban areas. View Full-Text
Keywords: urban classification; polarimetric SAR; time-frequency decomposition; decision tree urban classification; polarimetric SAR; time-frequency decomposition; decision tree
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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).

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Deng, L.; Yan, Y.-N.; Wang, C. Improved POLSAR Image Classification by the Use of Multi-Feature Combination. Remote Sens. 2015, 7, 4157-4177.

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