Deep Generative Models for Automated Dehazing Remote Sensing Satellite Images

Abstract

Remote Sensing (RS) is the process of observing and measuring the physical features of an area from a distance by monitoring its reflected and emitted radiation, usually from a satellite or aircraft. The application of RS spans a wide range of fields, including precision agriculture, disaster management, military operations, environmental monitoring, and weather assessment, among others. Haze or pollution in the satellite images, makes satellite images unsightly and makes valuable information useless. Sometimes satellites must capture images in haze-filled atmospheres, rendering them unusable for study. This proposed work is implemented using the Modern Deep Learning techniques to dehaze the satellite images. We have proposed two GAN architectures, INC-Pix2Pix and RNX-Pix2Pix. A publicly available dataset was used for training our proposed approaches. To eliminate haze from images, we have suggested Deep Generative models by employing the best developments in the field of image processing. By using generative models, images can be dehazed without information loss, supporting the paper’s objective. It has the capacity to learn any kind of underlying data distribution using its learning mechanism. Therefore, it can dehaze satellite images that have been corrupted by haze using the approach automated dehazing remote sensing satellite images using deep learning models . Existing systems can be made more efficient by integrating this approach.