Abstract
To recreate high-resolution, more detailed remote sensing images from existing low-resolution photos, this technique is known as remote sensing image superresolution reconstruction, and it has numerous uses. As an important research hotspot of neural networks, generative adversarial network (GAN) has made outstanding progress for image superresolution reconstruction. It solves the computational complexity and low reconstructed image quality of standard superresolution reconstruction algorithms. This research offers a superresolution reconstruction strategy with a self-attention generative adversarial network to improve the quality of reconstructed superresolution remote sensing images. The self-attention strategy as well as residual module is utilized to build a generator in this model that transforms low-resolution remote sensing images into superresolution ones. It aims to determine the discrepancy between a reconstructed picture and a true picture by using a deep convolutional network as a discriminator. For the purpose of enhancing the accuracy, content loss is used. This is done to obtain accurate detail reconstruction. According to the findings of the experiments, this approach is capable of regenerating higher-quality images.
Highlights
People’s expectations for image quality have risen in tandem with scientific and technological advancements and the widening of practical application domains
Superresolution reconstruction technology can use software algorithms to effectively improve the resolution of remote sensing images without being restricted by hardware devices
Most networks are for general images, and there are still many deficiencies and room for improvement in the reconstruction methods of remote sensing images
Summary
People’s expectations for image quality have risen in tandem with scientific and technological advancements and the widening of practical application domains. As a result, meeting the public’s desire for high-quality photographs is a top priority for image processing researchers. The richer feature information contained in the image provides a favorable basis for the various research and application of remote sensing images. In the remote sensing imaging process, the distance between the target and the imaging system is relatively long, and it is affected by some other factors. The quality of the obtained remote sensing image is often low, which makes the local area in the image blurred, part of the information is lost, and the target recognition is low, which cannot meet people’s data requirements [1–5]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
