Hyperspectral image classification using nearest feature line embedding approach

NCUIR > Research Center > Center for Space and Remote Sensing Research > journal & Dissertation > Item 987654321/101266

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/101266

Title:	Hyperspectral image classification using nearest feature line embedding approach
Authors:	陳映濃;Chang, Yang-Lang;Liu, Jin-Nan;Han, Chin-Chuan;Chen, Ying-Nong
Contributors:	太空及遙測研究中心
Keywords:	Applied geophysics;Classification;Earth sciences;Earth, ocean, space;Eigenspace projection;Exact sciences and technology;Feature extraction;hyperspectral images (HSI);Internal geophysics;land cover classification;Laplace equations;Manifolds;nearest linear line embedding;Prototypes;Remote sensing;Training;Vectors
Date:	2014-01-01
Issue Date:	2026-04-21 14:28:55 (UTC+8)
Publisher:	Institute of Electrical and Electronics Engineers Inc.;New York, NY: IEEE
Abstract:	摘要： Eigenspace projection methods are widely used for feature extraction from hyperspectral images (HSI) for the classification of land cover. Projection transformation is used to reduce higher dimensional feature vectors to lower dimensional vectors for more accurate classification of land cover types. In this paper, a nearest feature line embedding (NFLE) transformation is proposed for the dimension reduction (DR) of an HSI. The NFL measurement is embedded in the transformation during the discriminant analysis phase, instead of the matching phase. Three factors, including class separability, neighborhood structure preservation, and NFL measurement, are considered simultaneously to determine an effective and discriminating transformation in the eigenspaces for land cover classification. Three state-of-the-art classifiers, the nearest-neighbor, support vector machine, and NFL classifiers, were used to classify the reduced features. The proposed NFLE transformation is compared with different feature extraction approaches and evaluated using two benchmark data sets, the MASTER set at Au-Ku and the AVIRIS set at Northwest Tippecanoe County. The experimental results demonstrate that the NFLE approach is effective for DR in land cover classification in the field of Earth remote sensing. 其他題名： TGRS 出版者： New York, NY: IEEE 出版日期： 2014-01 出處： IEEE transactions on geoscience and remote sensing, 2014-01, Vol.52 (1), p.278-287 資源來源： IEEE Electronic Library (IEL) 版權： 2015 INIST-CNRS 版權： Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jan 2014 識別號： ISSN: 0196-2892 識別號： EISSN: 1558-0644 識別號： DOI: 10.1109/TGRS.2013.2238635 識別號： CODEN: IGRSD2
Appears in Collections:	[Center for Space and Remote Sensing Research ] journal & Dissertation

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