Remote Sensing Applications and Decision Support

Reweighted mass center based object-oriented sparse subspace clustering for hyperspectral images

[+] Author Affiliations
Han Zhai

Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, No. 129 Luoyu Road, Wuhan 430079, China

Hongyan Zhang, Liangpei Zhang, Pingxiang Li

Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, No. 129 Luoyu Road, Wuhan 430079, China

Wuhan University, Collaborative Innovation Center for Geospatial Technology, No. 129 Luoyu Road, Wuhan 430079, China

J. Appl. Remote Sens. 10(4), 046014 (Nov 23, 2016). doi:10.1117/1.JRS.10.046014
History: Received August 9, 2016; Accepted October 27, 2016
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Abstract.  Considering the inevitable obstacles faced by the pixel-based clustering methods, such as salt-and-pepper noise, high computational complexity, and the lack of spatial information, a reweighted mass center based object-oriented sparse subspace clustering (RMC-OOSSC) algorithm for hyperspectral images (HSIs) is proposed. First, the mean-shift segmentation method is utilized to oversegment the HSI to obtain meaningful objects. Second, a distance reweighted mass center learning model is presented to extract the representative and discriminative features for each object. Third, assuming that all the objects are sampled from a union of subspaces, it is natural to apply the SSC algorithm to the HSI. Faced with the high correlation among the hyperspectral objects, a weighting scheme is adopted to ensure that the highly correlated objects are preferred in the procedure of sparse representation, to reduce the representation errors. Two widely used hyperspectral datasets were utilized to test the performance of the proposed RMC-OOSSC algorithm, obtaining high clustering accuracies (overall accuracy) of 71.98% and 89.57%, respectively. The experimental results show that the proposed method clearly improves the clustering performance with respect to the other state-of-the-art clustering methods, and it significantly reduces the computational time.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Han Zhai ; Hongyan Zhang ; Liangpei Zhang and Pingxiang Li
"Reweighted mass center based object-oriented sparse subspace clustering for hyperspectral images", J. Appl. Remote Sens. 10(4), 046014 (Nov 23, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.046014


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