19 October 2012 Nearest feature line embedding approach to hyperspectral image classification
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Abstract
In this paper, a nearest feature line (NFL) embedding transformation is proposed for dimension reduction of hyperspectral image (HSI). Eigenspace projection approaches are generally used for feature extraction of HSI in remote sensing image classification. In order to improve the classification accuracy, the feature vectors of high dimensions are reduced to the low dimensionalities by the effective projection transformation. Similarly, the proposed NFL measurement is embedded into the transformation during the discriminant analysis stage instead of the matching stage. The class separability, neighborhood structure preservation, and NFL measurement are also simultaneously considered to find the effective and discriminating transformation in eigenspaces for image classification. The nearest neighbor classifier is used to show the discriminative performance. The proposed NFL embedding transformation is compared with several conventional state-of-the-art algorithms. It was evaluated by the AVIRIS data sets of Northwest Tippecanoe County. Experimental results have demonstrated that NFL embedding method is an effective transformation for dimension reduction in land cover classification of earth remote sensing.
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Yang-Lang Chang, Jin-Nan Liu, Chin-Chuan Han, Ying-Nong Chen, Tung-Ju Hsieh, Bormin Huang, "Nearest feature line embedding approach to hyperspectral image classification", Proc. SPIE 8514, Satellite Data Compression, Communications, and Processing VIII, 85140S (19 October 2012); doi: 10.1117/12.940740; https://doi.org/10.1117/12.940740
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