Unmixing-based Spatiotemporal Image Fusion Based on the Self-trained Random Forest Regression and Residual Compensation

Li, Xiaodong; Wang, Yalan; Zhang, Yihang; Hou, Shuwei; Zhou, Pu; Wang, Xia; Du, Yun; Foody, Giles

Abstract

Spatiotemporal satellite image fusion (STIF) has been widely applied in land surface monitoring to generate high spatial and high temporal reflectance images from satellite sensors. This paper proposed a new unmixing-based spatiotemporal fusion method that is composed of a self-trained random forest machine learning regression (R), low resolution (LR) endmember estimation (E), high resolution (HR) surface reflectance image reconstruction (R), and residual compensation (C), that is, RERC. RERC uses a self-trained random forest to train and predict the relationship between spectra and the corresponding class fractions. This process is flexible without any ancillary training dataset, and does not possess the limitations of linear spectral unmixing, which requires the number of endmembers to be no more than the number of spectral bands. The running time of the random forest regression is about ~1% of the running time of the linear mixture model. In addition, RERC adopts a spectral reflectance residual compensation approach to refine the fused image to make full use of the information from the LR image. RERC was assessed in the fusion of a prediction time MODIS with a Landsat image using two benchmark datasets, and was assessed in fusing images with different numbers of spectral bands by fusing a known time Landsat image (seven bands used) with a known time very-high-resolution PlanetScope image (four spectral bands). RERC was assessed in the fusion of MODIS-Landsat imagery in large areas at the national scale for the Republic of Ireland and France. The code is available at https://www.researchgate.net/proiile/Xiao_Li52.

Citation

Li, X., Wang, Y., Zhang, Y., Hou, S., Zhou, P., Wang, X., Du, Y., & Foody, G. (2023). Unmixing-based Spatiotemporal Image Fusion Based on the Self-trained Random Forest Regression and Residual Compensation. IEEE Transactions on Geoscience and Remote Sensing, 61, Article 5406319. https://doi.org/10.1109/tgrs.2023.3308902

• Journal Article Type: Article
• Acceptance Date: Aug 19, 2023
• Online Publication Date: Aug 28, 2023
• Publication Date: 2023
• Deposit Date: Aug 31, 2023
• Publicly Available Date: Aug 31, 2023
• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Print ISSN: 0196-2892
• Electronic ISSN: 1558-0644
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 61
• Article Number: 5406319
• DOI: https://doi.org/10.1109/tgrs.2023.3308902
• Keywords: Landsat, self-trained regression, spatiotemporal image fusion, sub-pixel analysis, unmixing
• Public URL: https://nottingham-repository.worktribe.com/output/24802411
• Publisher URL: https://ieeexplore.ieee.org/document/10231141