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

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Ma, Ping and Ren, Jinchang and Sun, Genyun and Zhao, Huimin and Jia, Xiuping and Yan, Yijun and Zabalza, Jaime (2023) Multiscale superpixelwise prophet model for noise-robust feature extraction in hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 61. 5508912. ISSN 0196-2892 (https://doi.org/10.1109/tgrs.2023.3260634)

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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.

ORCID iDs

Ma, Ping, Ren, Jinchang, Sun, Genyun, Zhao, Huimin, Jia, Xiuping, Yan, Yijun ORCID logoORCID: https://orcid.org/0000-0003-0224-0078 and Zabalza, Jaime ORCID logoORCID: https://orcid.org/0000-0002-0634-1725;
  • Item type:Article
    ID code:85172
    Dates:
    Date
    Event
    27 March 2023
    Published
    17 March 2023
    Accepted
    Notes:© 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
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:19 Apr 2023 10:08
    Last modified:18 Dec 2024 18:28
    URI:https://strathprints.strath.ac.uk/id/eprint/85172
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