Special Section on Sparsity-Driven High Dimensional Remote Sensing Image Processing and Analysis

Sparse coding-based correlaton model for land-use scene classification in high-resolution remote-sensing images

[+] Author Affiliations
Qi Kunlun

Wuhan University, State Key Laboratory of Water Resources and Hydropower Engineering Science, No. 299 BaYi Road, Wuhan 430072, China

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

Zhang Xiaochun

Wuhan University, State Key Laboratory of Water Resources and Hydropower Engineering Science, No. 299 BaYi Road, Wuhan 430072, China

Wu Baiyan

Hunan University of Science and Technology, School of Architecture and Urban Planning, Taoyuan Road, Xiangtan 411201, China

Wu Huayi

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 of Geospatial Technology, No. 129 Luoyu Road, Wuhan 430079, China

J. Appl. Remote Sens. 10(4), 042005 (Jun 20, 2016). doi:10.1117/1.JRS.10.042005
History: Received February 22, 2016; Accepted May 26, 2016
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Abstract.  High-resolution remote-sensing images are increasingly applied in land-use classification problems. Land-use scenes are often very complex and difficult to represent. Subsequently, the recognition of multiple land-cover classes is a continuing research question. We propose a classification framework based on a sparse coding-based correlaton (termed sparse correlaton) model to solve this challenge. Specifically, a general mapping strategy is presented to label visual words and generate sparse coding-based correlograms, which can exploit the spatial co-occurrences of visual words. A compact spatial representation without loss discrimination is achieved through adaptive vector quantization of correlogram in land-use scene classification. Moreover, instead of using K-means for visual word encoding in the original correlaton model, our proposed sparse correlaton model uses sparse coding to achieve lower reconstruction error. Experiments on a public ground truth image dataset of 21 land-use classes demonstrate that our sparse coding-based correlaton method can improve the performance of land-use scene classification and outperform many existing bag-of-visual-words-based methods.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Qi Kunlun ; Zhang Xiaochun ; Wu Baiyan and Wu Huayi
"Sparse coding-based correlaton model for land-use scene classification in high-resolution remote-sensing images", J. Appl. Remote Sens. 10(4), 042005 (Jun 20, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.042005


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