Hyperspectral imagery quality assessment and band reconstruction using the Prophet model.

Ma, Ping; Ren, Jinchang; Gao, Zhi; Li, Yinhe; Chen, Rongjun

Abstract

In Hyperspectral Imaging (HSI), the detrimental influence of noise and distortions on data quality is profound, which has severely affected the following-on analytics and decision-making such as land mapping. This study presents an innovative framework for assessing HSI band quality and reconstructing the low-quality bands, based on the Prophet model. By introducing a comprehensive quality metric to start, our approach factors in both spatial and spectral characteristics across local and global scales. This metric effectively captures the intricate noise and distortions inherent in the HSI data. Subsequently, we employ the Prophet model to forecast information within the low-quality bands, leveraging insights from neighboring high-quality bands. To validate the effectiveness of our proposed model, we conducted extensive experiments on three publicly available uncorrected datasets. In a head-to-head comparison, we benchmarked our framework against six state-of-the-art band reconstruction algorithms including three spectral methods, two spatial-spectral methods and one deep-learning method. Our experiments also delve into strategies for band selection based on quality metrics and the quality evaluation of the reconstructed bands. In addition, we assess the classification accuracy utilizing these reconstructed bands. In various experiments, our results consistently affirm the efficacy of our method in HSI quality assessment and band reconstruction. Notably, our approach obviates the need for manual prefiltering of noisy bands. This comprehensive framework holds promise in addressing HSI data quality concerns whilst enhancing the overall utility of HSI.

Citation

MA, P., REN, J., GAO, Z., LI, Y. and CHEN, R. [2024]. Hyperspectral imagery quality assessment and band reconstruction using the Prophet model. CAAI transactions on intelligence technology [online], Early View. Available from: https://doi.org/10.1049/cit2.12373

• Journal Article Type: Article
• Acceptance Date: Jan 3, 2024
• Online Publication Date: Sep 25, 2024
• Deposit Date: Jun 18, 2024
• Publicly Available Date: Jun 18, 2024
• Journal: CAAI transactions on intelligence technology
• Print ISSN: 2468-2322
• Electronic ISSN: 2468-2322
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1049/cit2.12373
• Keywords: Hyperspectral imaging; Electromagnetic spectrum bands; Band quality; Band reconstruction
• Public URL: https://rgu-repository.worktribe.com/output/2113431