2022
DOI: 10.1155/2022/9114911
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Superresolution Reconstruction of Remote Sensing Image Based on Generative Adversarial Network

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 o… Show more

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Cited by 4 publications
(3 citation statements)
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References 38 publications
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“…Notably, GAN is an excellent method to yield synthetic data samples using a small amount of real data within a short learning span, apart from augmenting training with synthetic data samples for cyber, computer vision, and text applications [351,572]. External impacts of waveform features, traffic, channel patterns, and interference are captured by GAN in wireless communication [573]. Augmentation of training data is executed using GAN for channel measurement in spectrum sensing [574], modulation classification [575], jamming [576], and call data records for 5G network [577].…”
Section: Wireless Communicationsmentioning
confidence: 99%
“…Notably, GAN is an excellent method to yield synthetic data samples using a small amount of real data within a short learning span, apart from augmenting training with synthetic data samples for cyber, computer vision, and text applications [351,572]. External impacts of waveform features, traffic, channel patterns, and interference are captured by GAN in wireless communication [573]. Augmentation of training data is executed using GAN for channel measurement in spectrum sensing [574], modulation classification [575], jamming [576], and call data records for 5G network [577].…”
Section: Wireless Communicationsmentioning
confidence: 99%
“…These methods are widely used in the fields of aeronautics, medicine, and engineering [14,15]. In the field of aviation, a variety of image super-resolution reconstruction algorithms had been proposed [16][17][18][19][20]. Zhou et al [18] proposed a super-resolution reconstruction strategy based on self-attention generative adversarial networks, which improves the details of remote sensing images by adding self-attention modules.…”
Section: Introductionmentioning
confidence: 99%
“…In the field of aviation, a variety of image super-resolution reconstruction algorithms had been proposed [16][17][18][19][20]. Zhou et al [18] proposed a super-resolution reconstruction strategy based on self-attention generative adversarial networks, which improves the details of remote sensing images by adding self-attention modules. Yue et al [21] proposed an improved enhanced super-resolution generative adversarial network (IESRGAN) based on enhanced U-Net structure, which is used to perform a four-fold scale detail reconstruction of LR images using NaSC-TG2 remote sensing images.…”
Section: Introductionmentioning
confidence: 99%