Enhancing super-resolution in remote sensing: Integrating GIS data with CNN-based SRGAN models for improved image reconstruction
Year: 2024
Type: article
Abstract: In the field of remote sensing (RS), image super-resolution (SR) techniques play a crucial role across various applications. Traditional SR methods face challenges when applied to long-term coverage datasets with limited spatial resolution. However, recent advancements in deep learning have opened up new possibilities for improving the spatial resolution of RS data. While many convolutional neural network (CNN)- based approaches have achieved excellent performance in developing efficient end-to-end SR models for natural images, they have been less frequently applied to satellite image upscaling with high scale factors. This paper introduces a novel CNN block that enhances the performance of SRGAN-based models. Experimental results show that these architectures benefit from additional data, especially when low-resolution images provide insufficient feature information.
Institution: Northern (Arctic) Federal University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6884555595
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