A multi-rule-based relative radiometric normalization for multi-sensor satellite images

Journal article

Relative radiometric normalization (RRN) is a widely used method for enhancing the radiometric consistency among multi-temporal satellite images. Diverse satellite images enhance the information for observing the Earth’s surface and bring additional uncertainties in the applications using multi-sensor images, such as change detection, multi-temporal analysis, image fusion, etc. To address this challenge, we developed a multi-rule-based RRN method for multi-sensor satellite images, which involves the identification of spectral- and spatial-invariant pseudo-invariant features (PIFs) and a Partial least-squares (PLS) regression-based RRN modeling using neighboring target pixels around PIFs. The proposed RRN method was validated on four datasets and demonstrated excellent effectiveness in identifying high-quality PIFs with spectral- and spatial-invariant properties, estimating precise regression models, and enhancing the radiometric consistency of reference-target image pair. Our method outperformed six RRN methods and effectively processed well-registered medium- and high-resolution images from the same sensor. This letter highlights the potential of our method for generating more comparable bi-temporal multi-sensor images.

Authors: Xu, H; Zhou, Y; Wei, Y; Guo, H; Li, X

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Journal: IEEE Geoscience and Remote Sensing Letters
• Volume: 20
• Article number: 5002105
• Publication date: 2023-07-24
• Acceptance date: 2023-07-02
• DOI: 10.1109/lgrs.2023.3298505
• EISSN: 1558-0571
• ISSN: 1545-598X
• Language: English
• Keywords: FFR