GAN-based E-D Network to Dehaze Satellite Images

Abstract

The intricate nature of remote sensing image dehazing poses a formidable challenge due to its multifaceted characteristics. Considered as a preliminary step for advanced remote sensing image tasks, haze removal becomes crucial. A novel approach is introduced with the objective of dehazing an image employing an encoder-decoder architecture embedded in a generative adversarial network (GAN). This innovative model systematically captures low-frequency information in the initial phase and subsequently assimilates high-frequency details from the remote sensing image. Incorporating a skip connection within the network serves the purpose of preventing information loss. To enhance the learning capability and assimilate more valuable insights, an additional component, the multi-scale attention module, is introduced. Drawing inspiration from multi-scale networks, an enhanced module is meticulously designed and incorporated at the network's conclusion. This augmentation methodology aims to further enhance the dehazing capabilities by assimilating context information across various scales. The material for fine-tuning the dehazing algorithm has been obtained from the RICE-I dataset that serves as the testing ground for a comprehensive comparison between our proposed method and other two alternative approaches. The experimental results distinctly showcase the superior efficacy of our method, both in qualitative and quantitative terms. Our proposed methodology performed better with respect to contemporary dehazing techniques in terms of PSNR and SSIM although it requires longer simulation times. So it could be concluded that we contributed a more comprehensive RS picture dehazing methodology to the existing dehazing methodology literature.