Nearest feature line embedding approach to hyperspectral image classification

Yang Lang Chang, Jin Nan Liu, Chin Chuan Han, Ying Nong Chen, Tung Ju Hsieh, Bormin Huang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

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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主出版物標題Satellite Data Compression, Communications, and Processing VIII
DOIs
出版狀態已出版 - 2012
事件Satellite Data Compression, Communications, and Processing VIII - San Diego, CA, United States
持續時間: 12 8月 201213 8月 2012

出版系列

名字Proceedings of SPIE - The International Society for Optical Engineering
8514
ISSN(列印)0277-786X

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???event.eventtypes.event.conference???Satellite Data Compression, Communications, and Processing VIII
國家/地區United States
城市San Diego, CA
期間12/08/1213/08/12

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