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Article

Hyperspectral Image Classification with Multi-Scale Feature Extraction

by 1,2,‡, 1,‡, 2,*, 1 and 3
1
School of Information Science and Technology, Hunan Institute of Science and Technology, Yueyang 414006, China
2
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
3
Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology (HIF), Exploration, 09599 Freiberg, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 20 January 2019 / Revised: 26 February 2019 / Accepted: 27 February 2019 / Published: 5 March 2019
(This article belongs to the Special Issue Multispectral Image Acquisition, Processing and Analysis)
Spectral features cannot effectively reflect the differences among the ground objects and distinguish their boundaries in hyperspectral image (HSI) classification. Multi-scale feature extraction can solve this problem and improve the accuracy of HSI classification. The Gaussian pyramid can effectively decompose HSI into multi-scale structures, and efficiently extract features of different scales by stepwise filtering and downsampling. Therefore, this paper proposed a Gaussian pyramid based multi-scale feature extraction (MSFE) classification method for HSI. First, the HSI is decomposed into several Gaussian pyramids to extract multi-scale features. Second, we construct probability maps in each layer of the Gaussian pyramid and employ edge-preserving filtering (EPF) algorithms to further optimize the details. Finally, the final classification map is acquired by a majority voting method. Compared with other spectral-spatial classification methods, the proposed method can not only extract the characteristics of different scales, but also can better preserve detailed structures and the edge regions of the image. Experiments performed on three real hyperspectral datasets show that the proposed method can achieve competitive classification accuracy. View Full-Text
Keywords: hyperspectral image classification; gaussian pyramid; multi-scale feature extraction hyperspectral image classification; gaussian pyramid; multi-scale feature extraction
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MDPI and ACS Style

Tu, B.; Li, N.; Fang, L.; He, D.; Ghamisi, P. Hyperspectral Image Classification with Multi-Scale Feature Extraction. Remote Sens. 2019, 11, 534. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11050534

AMA Style

Tu B, Li N, Fang L, He D, Ghamisi P. Hyperspectral Image Classification with Multi-Scale Feature Extraction. Remote Sensing. 2019; 11(5):534. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11050534

Chicago/Turabian Style

Tu, Bing, Nanying Li, Leyuan Fang, Danbing He, and Pedram Ghamisi. 2019. "Hyperspectral Image Classification with Multi-Scale Feature Extraction" Remote Sensing 11, no. 5: 534. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11050534

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