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Remote Sens. 2017, 9(3), 203;

Spectral-Spatial Response for Hyperspectral Image Classification

School of Educational Information Technology, Central China Normal University, Wuhan 430079, China
Department of Computer and Information Science, University of Macau, Taipa, Macau 999078, China
School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, China
Author to whom correspondence should be addressed.
Academic Editors: Giles M. Foody, Xiaofeng Li and Prasad S. Thenkabail
Received: 6 December 2016 / Accepted: 18 February 2017 / Published: 24 February 2017
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This paper presents a hierarchical deep framework called Spectral-Spatial Response (SSR) to jointly learn spectral and spatial features of Hyperspectral Images (HSIs) by iteratively abstracting neighboringregions. SSRformsadeeparchitectureandisabletolearndiscriminativespectral-spatial features of the input HSI at different scales. It includes several existing spectral-spatial-based methods as special scenarios within a single unified framework. Based on SSR, we further propose the Subspace Learning-based Networks (SLN) as an example of SSR for HSI classification. In SLN, the joint spectral and spatial features are learned using templates simply learned by Marginal Fisher Analysis (MFA) and Principal Component Analysis (PCA). An important contribution to the success of SLN is the exploitation of label information of training samples and the local spatial structure of HSI. Extensive experimental results on four challenging HSI datasets taken from the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS) airborne sensors show the implementational simplicity of SLN and verify the superiority of SSR for HSI classification. View Full-Text
Keywords: hierarchical framework; hyperspectral image classification; spectral-spatial feature; joint feature learning; subspace learning hierarchical framework; hyperspectral image classification; spectral-spatial feature; joint feature learning; subspace learning

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wei, Y.; Zhou, Y.; Li, H. Spectral-Spatial Response for Hyperspectral Image Classification. Remote Sens. 2017, 9, 203.

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