Research Papers

Classification of hyperspectral remote-sensing images based on sparse manifold learning

[+] Author Affiliations
Hong Huang

Chongqing University, Key Laboratory on Opto-electronic Technique and Systems, Ministry of Education, 400044 Chongqing, China

Technical Center of Chongqing Chuanyi Automation Co., Ltd., Chongqing 401121, China

J. Appl. Remote Sens. 7(1), 073464 (Dec 16, 2013). doi:10.1117/1.JRS.7.073464
History: Received July 3, 2013; Revised October 21, 2013; Accepted November 8, 2013
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Abstract.  Sparsity preserving projections (SPP) has drawn more and more attention recently. However, the SPP only focuses on the sparse structure but ignores the discriminant information of labeled samples. We proposed a new sparse manifold learning method, called sparse discriminant embedding (SDE), for hyperspectral image (HSI) classification. The SDE utilizes the merits of both sparsity property and manifold structure. It not only preserves the sparse reconstructive relations but also explicitly boosts the discriminant manifold structure of the data, and the discriminating power of the SDE is further improved than the SPP. Experiments on the Flightline C1, Washington DC Mall, and Botswana HSI datasets are performed to demonstrate the effectiveness of the proposed SDE method.

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© 2013 Society of Photo-Optical Instrumentation Engineers

Citation

Hong Huang
"Classification of hyperspectral remote-sensing images based on sparse manifold learning", J. Appl. Remote Sens. 7(1), 073464 (Dec 16, 2013). ; http://dx.doi.org/10.1117/1.JRS.7.073464


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