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Article

Polarimetric Contextual Classification of PolSAR Images Using Sparse Representation and Superpixels

School of Electronic Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
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Remote Sens. 2014, 6(8), 7158-7181; https://doi.org/10.3390/rs6087158
Received: 8 June 2014 / Revised: 16 July 2014 / Accepted: 18 July 2014 / Published: 31 July 2014
In recent years, sparse representation-based techniques have shown great potential for pattern recognition problems. In this paper, the problem of polarimetric synthetic aperture radar (PolSAR) image classification is investigated using sparse representation-based classifiers (SRCs). We propose to take advantage of both polarimetric information and contextual information by combining sparsity-based classification methods with the concept of superpixels. Based on polarimetric feature vectors constructed by stacking a variety of polarimetric signatures and a superpixel map, two strategies are considered to perform polarimetric-contextual classification of PolSAR images. The first strategy starts by classifying the PolSAR image with pixel-wise SRC. Then, spatial regularization is imposed on the pixel-wise classification map by using majority voting within superpixels. In the second strategy, the PolSAR image is classified by taking superpixels as processing elements. The joint sparse representation-based classifier (JSRC) is employed to combine the polarimetric information contained in feature vectors and the contextual information provided by superpixels. Experimental results on real PolSAR datasets demonstrate the feasibility of the proposed approaches. It is proven that the classification performance is improved by using contextual information. A comparison with several other approaches also verifies the effectiveness of the proposed approach. View Full-Text
Keywords: polarimetric synthetic aperture radar (PolSAR); image classification; sparse representation-based classifier; superpixel; spatial regularization; joint sparse representation polarimetric synthetic aperture radar (PolSAR); image classification; sparse representation-based classifier; superpixel; spatial regularization; joint sparse representation
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MDPI and ACS Style

Feng, J.; Cao, Z.; Pi, Y. Polarimetric Contextual Classification of PolSAR Images Using Sparse Representation and Superpixels. Remote Sens. 2014, 6, 7158-7181. https://doi.org/10.3390/rs6087158

AMA Style

Feng J, Cao Z, Pi Y. Polarimetric Contextual Classification of PolSAR Images Using Sparse Representation and Superpixels. Remote Sensing. 2014; 6(8):7158-7181. https://doi.org/10.3390/rs6087158

Chicago/Turabian Style

Feng, Jilan, Zongjie Cao, and Yiming Pi. 2014. "Polarimetric Contextual Classification of PolSAR Images Using Sparse Representation and Superpixels" Remote Sensing 6, no. 8: 7158-7181. https://doi.org/10.3390/rs6087158

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