This paper explores the application of deep learning approaches in the field of remote sensing for image super-resolution (ISR). The demand for high-resolution imagery in remote sensing has been increasing, as it offers a wealth of valuable information for numerous applications such as environmental monitoring, urban planning, and agricultural assessment. However, the acquisition of high-resolution images often requires expensive sensors and time-consuming processes. Deep learning has emerged as a powerful tool for enhancing the resolution of low-quality images, and this study investigates its efficacy in remote sensing ISR. By leveraging convolutional neural networks (CNNs) and generative adversarial networks (GANs), this paper presents a comparative analysis of their performance in super-resolution tasks. The experimental results demonstrate that deep learning models, particularly those based on GANs, can significantly improve the quality of super-resolved images while maintaining the details and textures. In addition, the paper discusses the limitations of existing models and suggests potential avenues for future research, including the integration of transfer learning and the development of more efficient architectures for ISR in remote sensing.
Jackson, M. Deep Learning Approaches for Image Super-Resolution in Remote Sensing. Information Sciences and Technological Innovations, 2021, 3, 21. https://doi.org/10.69610/j.isti.20210821
AMA Style
Jackson M. Deep Learning Approaches for Image Super-Resolution in Remote Sensing. Information Sciences and Technological Innovations; 2021, 3(2):21. https://doi.org/10.69610/j.isti.20210821
Chicago/Turabian Style
Jackson, Michael 2021. "Deep Learning Approaches for Image Super-Resolution in Remote Sensing" Information Sciences and Technological Innovations 3, no.2:21. https://doi.org/10.69610/j.isti.20210821
APA style
Jackson, M. (2021). Deep Learning Approaches for Image Super-Resolution in Remote Sensing. Information Sciences and Technological Innovations, 3(2), 21. https://doi.org/10.69610/j.isti.20210821
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