RDC-UNet++: An end-to-end network for multispectral satellite image enhancement

• Published in: Remote sensing applications 2024-11, Vol.36, p.101293, Article 101293
• Main Authors: Suresh, Shilpa, M., Ragesh Rajan, C.S., Asha, Dell’Acqua, Fabio
• Format: Article
• Language: English
• Subjects: Deep learning; Multi-spectral satellite images; Remote sensing; UNet
• ISSN: 2352-9385; 2352-9385
• DOI: 10.1016/j.rsase.2024.101293

Multi-spectral satellite imagery is an ideal data source for comprehensive, real-time Earth observation (EO) due to its extensive coverage of Earth and regular updates. It has a wide range of applications in environment monitoring, disaster management, urban planning, weather forecasting etc. Yet, the visual aspect of these images and thus the possibility to extract useful information using image processing techniques is often degraded due to fog, rain, dust, cloud, etc. Satellite image enhancement denotes a set of techniques designed to improve the quality of a satellite image such that the result is more useful for image analysis. The image enhancement aims to improve the quality of an image such that the enhanced image is more useful for image analysis than the original image for a particular remote sensing application. This study introduces a multi-spectral satellite image enhancement architecture called Residual Dense Connection-based UNet++ (RDC-UNet++). The unique design can improve multi-spectral images by enhancing their color and texture details. Extensive experimental studies on multi-spectral image datasets containing more than 150 images prove that the proposed architecture performs better than recent state-of-the-art satellite image enhancement algorithms.