Year: 2025
Type: article
Abstract: Remote sensing imagery is frequently degraded by dense haze and thin clouds, which hinder accurate Earth observation and downstream analysis. In this paper, we propose a densely connected U-Net-based deep learning architecture tailored for haze and cloud removal in satellite images. The proposed model is trained using available RS-Haze datasets and transfer learning was performed in Cartosat-2E MX, indian Satellite data. The model outcome was evaluated using a combined SSIM and MSE loss to preserve structural integrity and pixel- level detail. After training for 200 epochs, the model demonstrates strong generalization across varying atmospheric conditions, effectively removing dense haze while preserving critical land features such as river boundaries, urban layouts, and agricultural zones. Quantitative results confirm improvements in PSNR and SSIM over existing baselines, and qualitative assessments further validate the model’s capability to enhance image clarity for remote sensing applications. The proposed approach offers a promising tool for improving the image clarity for geospatial analytics in hazy environments.
Author: Sanyam Antil
Institution: Bennett University
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: diamond
OpenAlex ID: W4413193920