Multi-spectral Gradient Residual Network for Haze Removal in Multi-sensor Remote Sensing Imagery

Xian Yang, Ranga Raju Vatsavai

Abstract: Remote sensing imagery is widely used for various Earth surface monitoring applications. However, the quality of these observations can be degraded by clouds during image acquisition. Haze, a type of thin cloud, commonly causes atmospheric absorption and scattering of visible light, resulting in partially obscured regions. Haze removal is an active research area with two main approaches: physics-driven computer vision and end-to-end data-driven machine learning. To leverage both approaches, we propose a deep neural network framework that utilizes large-scale multi-sensor data and geometric knowledge from image physics. This is achieved through a multi-spectral gradient residual network. This network transfers structural details from near-infrared (NIR) images, which have better haze penetration, to the visible (RGB) bands. During training, we incorporate a soft constraint using the partially available information under haze conditions. This constraint helps the model maintain atmospheric consistency, a concept commonly used in physical haze models. We validated our model’s performance on a multi-sensor benchmark dataset containing Landsat-8 and Sentinel-2 satellite images. Comparisons with state-of-the-art methods demonstrate significant improvements. Our model achieves a minimum of 18.71% improvement on MSE, 25.9% on SSIM, and 6.25% on MS-SSIM compared to the next best method. It also shows advancements in LPIPS (14.43%) and SAM (8.47%) measures.

External IDs:dblp:conf/pkdd/YangV24