Single Frame Super Resolution with Convolutional Neural Network for Remote Sensing Imagery

Fu, Jie; Liu, Yuhong; Li, Feng

doi:10.1109/igarss.2018.8518584

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…Initial studies on single-frame SR for remote sensing images used simple structures like 3D Full Convolutional Neural Network (3D-FCNN) and Remote Sensing Image Convolutional Neural Network (RSCNN) with direct superposition of convolutional layers [24][25][26] as their basis. An unsupervised depth generating network was developed by Haut et al [27]in order to overcome the lack of benchmark training data for super-resolution remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Resolution of FY4 Remote Sensing Visible Spectrum Images Utilizing Super-Resolution and Transfer Learning Techniques

Zhang

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Remote sensing images acquired by the FY4 satellite are crucial for regional cloud monitoring and meteorological services. Inspired by the success of deep learning networks in image superresolution, we applied image super-resolution to FY4 visible spectrum (VIS) images. However, training a robust network directly for FY4 VIS image super-resolution remains challenging due to the limited provision of high resolution FY4 sample data. Here, we propose a super-resolution and transfer learning model, FY4-SR-Net. It is composed of pre-training and fine-tuning models. The pre-training model was developed using a deep residual network and a large number of FY4 A 4km and 1km resolution VIS images as the training data. The knowledge derived from 4 km to 1 km resolution images was incorporated into FY4 B 1 km to 0.25 km resolution VIS images. The FY4-SR-Net is finetuned by incorporating limited 1km and 0.25km resolution panchromatic (PAN) images, and then producing 1km superresolution VIS images of the FY4 satellite. Using the one-day FY4 test dataset for qualitative and quantitative evaluations, the FY4-SR-Net outperformed the classic bicubic interpolation approach with a 16.12% reduction in root mean square error (RMSE) and a 2.97% rise in peak signal-to-noise ratio (PSNR) averages. The structural similarity (SSIM) value average increased by 0.0026. This work provides a new precedent for improving the spatial resolution of FY4 series meteorological satellites, which has important scientific significance and application properties.

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Section: Introductionmentioning

confidence: 99%

Enhanced Resolution of FY4 Remote Sensing Visible Spectrum Images Utilizing Super-Resolution and Transfer Learning Techniques

Zhang

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The applications of super-resolution algorithms may be found in multiple research studies. The authors of [18] applied SRCNN and VDSR solutions for Pleiades and SPOT imagery. On the other hand, Lanaras et al [19] increased the resolution of channels in the Sentinel 2 imagery from 20 m and 60 m to 10 m with the use of high-resolution bands in order to transfer spatial details from lower-resolution channels.…”

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confidence: 99%

Modified ESRGAN with Uformer for Video Satellite Imagery Super-Resolution

Karwowska,

Wierzbicki

2024

Remote Sensing

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In recent years, a growing number of sensors that provide imagery with constantly increasing spatial resolution are being placed on the orbit. Contemporary Very-High-Resolution Satellites (VHRS) are capable of recording images with a spatial resolution of less than 0.30 m. However, until now, these scenes were acquired in a static way. The new technique of the dynamic acquisition of video satellite imagery has been available only for a few years. It has multiple applications related to remote sensing. However, in spite of the offered possibility to detect dynamic targets, its main limitation is the degradation of the spatial resolution of the image that results from imaging in video mode, along with a significant influence of lossy compression. This article presents a methodology that employs Generative Adversarial Networks (GAN). For this purpose, a modified ESRGAN architecture is used for the spatial resolution enhancement of video satellite images. In this solution, the GAN network generator was extended by the Uformer model, which is responsible for a significant improvement in the quality of the estimated SR images. This enhances the possibilities to recognize and detect objects significantly. The discussed solution was tested on the Jilin-1 dataset and it presents the best results for both the global and local assessment of the image (the mean values of the SSIM and PSNR parameters for the test data were, respectively, 0.98 and 38.32 dB). Additionally, the proposed solution, in spite of the fact that it employs artificial neural networks, does not require a high computational capacity, which means it can be implemented in workstations that are not equipped with graphic processors.

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A Rooftop-Contour Guided 3D Reconstruction Texture Mapping Method for Building using Satellite Images

Zhang

Chen

et al. 2022

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

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Single Frame Super Resolution with Convolutional Neural Network for Remote Sensing Imagery

Cited by 7 publications

References 14 publications

Enhanced Resolution of FY4 Remote Sensing Visible Spectrum Images Utilizing Super-Resolution and Transfer Learning Techniques

Enhanced Resolution of FY4 Remote Sensing Visible Spectrum Images Utilizing Super-Resolution and Transfer Learning Techniques

Modified ESRGAN with Uformer for Video Satellite Imagery Super-Resolution

A Rooftop-Contour Guided 3D Reconstruction Texture Mapping Method for Building using Satellite Images

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