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Article

Hyperspectral Image Denoising and Classification Using Multi-Scale Weighted EMAPs and Extreme Learning Machine

1
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
2
School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
3
School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510641, China
4
GRGBanking Equipment Co., Ltd., Guangzhou 510663, China
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(12), 2137; https://doi.org/10.3390/electronics9122137
Received: 1 November 2020 / Revised: 5 December 2020 / Accepted: 10 December 2020 / Published: 14 December 2020
(This article belongs to the Section Artificial Intelligence)
Recently, extended multi-attribute profiles (EMAPs) have attracted much attention due to its good performance while applied to remote sensing images feature extraction and classification. Since the EMAPs connect multiple attribute features without considering the pixel-based Hyperspectral Image (HSI) classification, homogeneous regions may become unsmooth due to the noise to be introduced. To tackle this problem, we propose the weighted EMAPs (WEMAPs) to reduce the noise and smoothen the homogeneous regions based on weighted mean filter (WMF). Then, we construct multiscale WEMAPs to product multiscale feature in order to extract different spatial structures of the HSI and produce better classification results. Finally, a new joint decision fusion and feature fusion (JDFFF) framework is proposed based on the decision fusion (DF) and the multiscale WEMAPs (MWEMAPs) based on extreme learning machine (ELM) classifier. That is, the classification results from various scales are combined into a final one with ELM to perform the HSI classification. Experiment results show that the proposed algorithm significantly outperforms many state-of-the-art HSI classification algorithms. View Full-Text
Keywords: extreme learning machine (ELM); decision fusion (DF); hyperspectral image (HSI) classification; multiscale weighted extended multi-attribute profiles (MWEMAPs); weighted mean filters (WMFs) extreme learning machine (ELM); decision fusion (DF); hyperspectral image (HSI) classification; multiscale weighted extended multi-attribute profiles (MWEMAPs); weighted mean filters (WMFs)
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MDPI and ACS Style

Liu, M.; Cao, F.; Yang, Z.; Hong, X.; Huang, Y. Hyperspectral Image Denoising and Classification Using Multi-Scale Weighted EMAPs and Extreme Learning Machine. Electronics 2020, 9, 2137. https://doi.org/10.3390/electronics9122137

AMA Style

Liu M, Cao F, Yang Z, Hong X, Huang Y. Hyperspectral Image Denoising and Classification Using Multi-Scale Weighted EMAPs and Extreme Learning Machine. Electronics. 2020; 9(12):2137. https://doi.org/10.3390/electronics9122137

Chicago/Turabian Style

Liu, Meizhuang; Cao, Faxian; Yang, Zhijing; Hong, Xiaobin; Huang, Yuezhen. 2020. "Hyperspectral Image Denoising and Classification Using Multi-Scale Weighted EMAPs and Extreme Learning Machine" Electronics 9, no. 12: 2137. https://doi.org/10.3390/electronics9122137

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