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Remote Sens. 2017, 9(1), 10; doi:10.3390/rs9010010

Multi-Task Joint Sparse and Low-Rank Representation for the Scene Classification of High-Resolution Remote Sensing Image

1
National Engineering Research Center of Geographic Information System, China University of Geosciences (Wuhan), Wuhan 430074, China
2
Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
3
State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan 430079, China
4
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editors: Xiaofeng Li and Prasad S. Thenkabail
Received: 3 November 2016 / Revised: 16 December 2016 / Accepted: 21 December 2016 / Published: 27 December 2016
View Full-Text   |   Download PDF [4657 KB, uploaded 27 December 2016]   |  

Abstract

Scene classification plays an important role in the intelligent processing of High-Resolution Satellite (HRS) remotely sensed images. In HRS image classification, multiple features, e.g., shape, color, and texture features, are employed to represent scenes from different perspectives. Accordingly, effective integration of multiple features always results in better performance compared to methods based on a single feature in the interpretation of HRS images. In this paper, we introduce a multi-task joint sparse and low-rank representation model to combine the strength of multiple features for HRS image interpretation. Specifically, a multi-task learning formulation is applied to simultaneously consider sparse and low-rank structures across multiple tasks. The proposed model is optimized as a non-smooth convex optimization problem using an accelerated proximal gradient method. Experiments on two public scene classification datasets demonstrate that the proposed method achieves remarkable performance and improves upon the state-of-art methods in respective applications. View Full-Text
Keywords: multi-task learning; feature fusion; sparse representation; low-rank representation; scene classification multi-task learning; feature fusion; sparse representation; low-rank representation; scene 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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Qi, K.; Liu, W.; Yang, C.; Guan, Q.; Wu, H. Multi-Task Joint Sparse and Low-Rank Representation for the Scene Classification of High-Resolution Remote Sensing Image. Remote Sens. 2017, 9, 10.

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