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

Hong Huang

doi:10.1117/1.JRS.7.073464

16 December 2013 Classification of hyperspectral remote-sensing images based on sparse manifold learning

Hong Huang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 7, Issue 1, 073464 (December 2013). https://doi.org/10.1117/1.JRS.7.073464

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.

Citation Download Citation

Hong Huang "Classification of hyperspectral remote-sensing images based on sparse manifold learning," Journal of Applied Remote Sensing 7(1), 073464 (16 December 2013). https://doi.org/10.1117/1.JRS.7.073464

Published: 16 December 2013

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