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Open AccessArticle

A Novel Analysis Dictionary Learning Model Based Hyperspectral Image Classification Method

1
School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
2
The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi’an 710072, China
3
School of Computer Science, The University of Adelaide, Adelaide 5000, Australia
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(4), 397; https://doi.org/10.3390/rs11040397
Received: 31 December 2018 / Revised: 3 February 2019 / Accepted: 14 February 2019 / Published: 15 February 2019
Supervised hyperspectral image (HSI) classification has been acknowledged as one of the fundamental tasks of hyperspectral data analysis. Witnessing the success of analysis dictionary learning (ADL)-based method in recent years, we propose an ADL-based supervised HSI classification method in this paper. In the proposed method, the dictionary is modeled considering both the characteristics within the spectrum and among the spectra. Specifically, to reduce the influence of strong nonlinearity within each spectrum on classification, we divide the spectrum into some segments, and based on this we propose HSI classification strategy. To preserve the relationships among spectra, similarities among pixels are introduced as constraints. Experimental results on several benchmark hyperspectral datasets demonstrate the effectiveness of the proposed method for HSI classification. View Full-Text
Keywords: dictionary learning; sparse representation; hyperspectral image classification; supervised method dictionary learning; sparse representation; hyperspectral image classification; supervised method
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Wei, W.; Ma, M.; Wang, C.; Zhang, L.; Zhang, P.; Zhang, Y. A Novel Analysis Dictionary Learning Model Based Hyperspectral Image Classification Method. Remote Sens. 2019, 11, 397.

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