Iterative detail-preserving thin-cloud removal method for panchromatic remote sensing images

Abstract

Optical remote sensing images are frequently affected by clouds, haze, and mist in the atmosphere. We introduce an iterative minimization light-cloud removal method designed for the specific quality improvement needs of military reconnaissance panchromatic remote sensing images. The proposed method is required to fulfill the military reconnaissance demands for improvements in the quality of panchromatic high-resolution images while guaranteeing high fidelity between the restored and observed images. A heuristic approach based on contrast enhancement is proposed to solve the thin-cloud removal problem. We design the target function of a minimization algorithm that contains a fidelity term, a contrast penalty term, and an information loss penalty term. By minimizing the target function with the iterative steepest descent method, a high-quality image can be restored from the observed satellite cloudy image, and the details are preserved by the penalty terms. The application of our iterative method to Gaofen-1 (GF-1) and Ziyuan-3 (ZY-3) satellite data shows that the iterative method was applicable to GF-1 and ZY-3 satellite and the data showed that for panchromatic remote sensing images, the proposed method could reduce satellite image degradation caused by haze and thin clouds while preserving the details in the observed images.