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Open AccessArticle

A Dimension Reduction Framework for HSI Classification Using Fuzzy and Kernel NFLE Transformation

1
Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan
2
Department of Information Management, National United University, Miaoli 36063, Taiwan
3
Department of Computer Science and Information Engineering, National United University, Miaoli 36063, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Yuei-An Liou, Chyi-Tyi Lee, Yaoming Ma, Takashi Oguchi, Indrajeet Chaubey, Giles M. Foody and Prasad S. Thenkabail
Remote Sens. 2015, 7(11), 14292-14326; https://doi.org/10.3390/rs71114292
Received: 23 May 2015 / Revised: 16 October 2015 / Accepted: 22 October 2015 / Published: 29 October 2015
(This article belongs to the Special Issue Earth Observations for the Sustainable Development)
In this paper, a general nearest feature line (NFL) embedding (NFLE) transformation called fuzzy-kernel NFLE (FKNFLE) is proposed for hyperspectral image (HSI) classification in which kernelization and fuzzification are simultaneously considered. Though NFLE has successfully demonstrated its discriminative capability, the non-linear manifold structure cannot be structured more efficiently by linear scatters using the linear NFLE method. According to the proposed scheme, samples were projected into a kernel space and assigned larger weights based on that of their neighbors. The within-class and between-class scatters were calculated using the fuzzy weights, and the best transformation was obtained by maximizing the Fisher criterion in the kernel space. In that way, the kernelized manifold learning preserved the local manifold structure in a Hilbert space as well as the locality of the manifold structure in the reduced low-dimensional space. The proposed method was compared with various state-of-the-art methods to evaluate the performance using three benchmark data sets. Based on the experimental results: the proposed FKNFLE outperformed the other, more conventional methods. View Full-Text
Keywords: hyperspectral image classification; manifold learning; nearest feature line embedding; kernelization; fuzzification hyperspectral image classification; manifold learning; nearest feature line embedding; kernelization; fuzzification
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MDPI and ACS Style

Chen, Y.-N.; Hsieh, C.-T.; Wen, M.-G.; Han, C.-C.; Fan, K.-C. A Dimension Reduction Framework for HSI Classification Using Fuzzy and Kernel NFLE Transformation. Remote Sens. 2015, 7, 14292-14326. https://doi.org/10.3390/rs71114292

AMA Style

Chen Y-N, Hsieh C-T, Wen M-G, Han C-C, Fan K-C. A Dimension Reduction Framework for HSI Classification Using Fuzzy and Kernel NFLE Transformation. Remote Sensing. 2015; 7(11):14292-14326. https://doi.org/10.3390/rs71114292

Chicago/Turabian Style

Chen, Ying-Nong; Hsieh, Cheng-Ta; Wen, Ming-Gang; Han, Chin-Chuan; Fan, Kuo-Chin. 2015. "A Dimension Reduction Framework for HSI Classification Using Fuzzy and Kernel NFLE Transformation" Remote Sens. 7, no. 11: 14292-14326. https://doi.org/10.3390/rs71114292

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