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Remote Sens. 2017, 9(7), 660; https://doi.org/10.3390/rs9070660

PolSAR Land Cover Classification Based on Roll-Invariant and Selected Hidden Polarimetric Features in the Rotation Domain

The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China
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Academic Editors: Timo Balz, Uwe Soergel, Mattia Crespi, Batuhan Osmanoglu and Prasad S. Thenkabail
Received: 8 May 2017 / Revised: 9 June 2017 / Accepted: 15 June 2017 / Published: 1 July 2017
(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications)
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Abstract

Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR). Target polarimetric response is strongly dependent on its orientation. Backscattering responses of the same target with different orientations to the SAR flight path may be quite different. This target orientation diversity effect hinders PolSAR image understanding and interpretation. Roll-invariant polarimetric features such as entropy, anisotropy, mean alpha angle, and total scattering power are independent of the target orientation and are commonly adopted for PolSAR image classification. On the other aspect, target orientation diversity also contains rich information which may not be sensed by roll-invariant polarimetric features. In this vein, only using the roll-invariant polarimetric features may limit the final classification accuracy. To address this problem, this work uses the recently reported uniform polarimetric matrix rotation theory and a visualization and characterization tool of polarimetric coherence pattern to investigate hidden polarimetric features in the rotation domain along the radar line of sight. Then, a feature selection scheme is established and a set of hidden polarimetric features are selected in the rotation domain. Finally, a classification method is developed using the complementary information between roll-invariant and selected hidden polarimetric features with a support vector machine (SVM)/decision tree (DT) classifier. Comparison experiments are carried out with NASA/JPL AIRSAR and multi-temporal UAVSAR data. For AIRSAR data, the overall classification accuracy of the proposed classification method is 95.37% (with SVM)/96.38% (with DT), while that of the conventional classification method is 93.87% (with SVM)/94.12% (with DT), respectively. Meanwhile, for multi-temporal UAVSAR data, the mean overall classification accuracy of the proposed method is up to 97.47% (with SVM)/99.39% (with DT), which is also higher than the mean accuracy of 89.59% (with SVM)/97.55% (with DT) from the conventional method. The comparison studies clearly demonstrate the efficiency and advantage of the proposed classification methodology. In addition, the proposed classification method achieves better robustness for the multi-temporal PolSAR data. This work also further validates that added benefits can be gained for PolSAR data investigation by mining and utilization of hidden polarimetric information in the rotation domain. View Full-Text
Keywords: polarimetric synthetic aperture radar (PolSAR); polarimetric feature; polarimetric matrix rotation; polarimetric coherence pattern; rotation domain; feature selection; classification polarimetric synthetic aperture radar (PolSAR); polarimetric feature; polarimetric matrix rotation; polarimetric coherence pattern; rotation domain; feature selection; classification
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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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Tao, C.; Chen, S.; Li, Y.; Xiao, S. PolSAR Land Cover Classification Based on Roll-Invariant and Selected Hidden Polarimetric Features in the Rotation Domain. Remote Sens. 2017, 9, 660.

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