Skip to main content

Research Repository

Advanced Search

Multiscale superpixelwise prophet model for noise-robust feature extraction in hyperspectral images.

Ma, Ping; Ren, Jinchang; Sun, Genyun; Zhao, Huimin; Jia, Xiuping; Yan, Yijun; Zabalza, Jaime

Authors

Genyun Sun

Huimin Zhao

Xiuping Jia

Jaime Zabalza



Abstract

Despite of various approaches proposed to smooth the hyperspectral images (HSIs) before feature extraction, the efficacy is still affected by the noise, even using the corrected dataset with the noisy and water absorption bands discarded. In this study, a novel spectral-spatial feature mining framework, Multiscale Superpixelwise Prophet Model (MSPM), is proposed for noise-robust feature extraction and effective classification of the HSI. The prophet model is highly noise-robust for deeply digging into the complex structured features thus enlarging interclass diversity and improving intraclass similarity. First, the superpixelwise segmentation is produced from the first three principal components of an HSI to group pixels into regions with adaptively determined sizes and shapes. A multiscale prophet model is utilized to extract the multiscale informative trend components from the average spectrum of each superpixel. Taking the multiscale trend signal as the input feature, the HSI data are classified superpixelwisely, which is further refined by a majority vote based decision fusion. Comprehensive experiments on three publicly available datasets have fully validated the efficacy and robustness of our MSPM model when benchmarked with eleven state-of-the-art algorithms, including six spectral-spatial methods and five deep learning ones. Besides, MSPM also shows superiority under limited training samples, due to the combined strategies of superpixelwise fusion and multiscale fusion. Our model has provided a useful solution for noise-robust feature extraction as it achieves superior HSI classification even from the uncorrected dataset without prefiltering the water absorption and noisy bands.

Citation

MA, P., REN, J., SUN, G., ZHAO, H., JIA, X., YAN, Y. and ZABALZA, J. 2023. Multiscale superpixelwise prophet model for noise-robust feature extraction in hyperspectral images. IEEE transactions on geoscience and remote sensing [online], 61, article 5508912. Available from: https://doi.org/10.1109/TGRS.2023.3260634

Journal Article Type Article
Acceptance Date Mar 17, 2023
Online Publication Date Mar 27, 2023
Publication Date Dec 31, 2023
Deposit Date Apr 4, 2023
Publicly Available Date Apr 4, 2023
Journal IEEE transactions on geoscience and remote sensing
Print ISSN 0196-2892
Electronic ISSN 1558-0644
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
Volume 61
Article Number 5508912
DOI https://doi.org/10.1109/TGRS.2023.3260634
Keywords Analytical models; Data mining; Data models; Feature extraction; Hyperspectral image (HSI); Market research; Multiscale prophet model; Noise robustness; Spectral-spatial feature mining; Superpixel segmentation; Training
Public URL https://rgu-repository.worktribe.com/output/1925440
Related Public URLs https://rgu-repository.worktribe.com/output/1930754 (Supplementary material)

Files

MA 2023 Multiscale superpixelwise prophet (AAM) (1.3 Mb)
PDF

Copyright Statement
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.





You might also like



Downloadable Citations