Color-Distortion Correction for Jilin-1 KF01 Series Satellite Imagery Using a Data-Driven Method.

Color distortion is a common issue in Jilin-1 KF01 series satellite imagery, a phenomenon caused by the instability of the sensor during the imaging process. In this paper, we propose a data-driven method to correct color distortion in Jilin-1 KF01 imagery. Our method involves three key aspects: color-distortion simulation, model design, and post-processing refinement. First, we investigate the causes of color distortion and propose algorithms to simulate this phenomenon. By superimposing simulated color-distortion patterns onto clean images, we construct color-distortion datasets comprising a large number of paired images (distorted–clean) for model training. Next, we analyze the principles behind a denoising model and explore its feasibility for color-distortion correction. Based on this analysis, we train the denoising model from scratch using the color-distortion datasets and successfully adapt it to the task of color-distortion correction in Jilin-1 KF01 imagery. Finally, we propose a novel post-processing algorithm to remove boundary artifacts caused by block-wise image processing, ensuring consistency and quality across the entire image. Experimental results show that the proposed method significantly eliminates color distortion and enhances the radiometric quality of Jilin-1 KF01 series satellite imagery, offering a solution for improving its usability in remote sensing applications. [ABSTRACT FROM AUTHOR]