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

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)