GENERATIVE ADVERSARIAL NETWORKS FOR REMOTE SENSING IMAGE DEHAZING WITH COLOR FEATURE RESTORATION

Remote sensing images are often affected by atmospheric factors such as haze during the acquisition process, resulting in blurring and low contrast in the collected remote sensing images. This problem impacts the image quality, thereby affecting the analysis of remote sensing images. To mitigate the impact of haze on remote sensing images, a generative adversarial network is proposed. It comprises a generative network and an adversarial network. Firstly, a novel feature extraction module is designed to enhance the capability of extracting useful information from remote sensing images. It enables the network to focus more on regions with dense haze, allowing it to extract more important information while filtering out irrelevant details. Secondly, a residual attention module is designed which can allocate different weights based on varying haze density in the feature map. This module readjusts features outputted by the encoder, facilitating better image restoration. Thirdly, a multi-scale module is also incorporated to extract feature information across various image scales. Lastly, a color feature extraction module is designed to extract color features. The novel feature extraction module, residual attention module, multi-scale module, and color feature extraction module are utilized for constructing the generative network. Besides, an adversarial network is also designed to indirectly enhance the dehazing capability of the generative network. Synthetic and real datasets are used to test six different methods for dehazing remote sensing images, respectively. The proposed method achieves higher PSNR, SSIM, and lower MSE on the synthetic remote sensing dataset. On the other hand, it achieves lower PIQE, BRISQUE, and higher MetaIQA on the real remote sensing dataset. The proposed method has best performance in dehazing remote sensing images than other methods. © 2025, Int. J. Innov. Comput. Inf. Control. All rights reserved.