A Multi-rule-based Relative Radiometric Normalization for Multi-Sensor Satellite Images

Abstract

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.