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Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer

arXiv 2105.07322 ↗First posted 16 May 2021cs.CV cs.LG eess.IVcited by 0

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Abstract (arXiv)

Urban material recognition in remote sensing imagery is a highly relevant, yet extremely challenging problem due to the difficulty of obtaining human annotations, especially on low resolution satellite images. To this end, we propose an unsupervised domain adaptation based approach using adversarial learning. We aim to harvest information from smaller quantities of high resolution data (source domain) and utilize the same to super-resolve low resolution imagery (target domain). This can potentially aid in semantic as well as material label transfer from a richly annotated source to a target domain.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 38 references that are in the map. First posted on arXiv 16 May 2021.

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17 of 38 references.

References · 17

  1. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  2. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  3. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
  4. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,199
  5. 2016Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networkscited by 307
  6. 2018Efficient parametrization of multi-domain deep neural networkscited by 168
  7. 2018Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networkscited by 133
  8. 2018DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptationcited by 101
  9. 2017ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenescited by 95
  10. 2018Fully Convolutional Adaptation Networks for Semantic Segmentationcited by 75
  11. 2018Learning Monocular Depth by Distilling Cross-domain Stereo Networkscited by 64
  12. 2017Cross-Domain Self-supervised Multi-task Feature Learning using Synthetic Imagerycited by 56
5 more
  1. 2017Adversarial Feature Augmentation for Unsupervised Domain Adaptationcited by 48
  2. 2016Procedural Generation of Videos to Train Deep Action Recognition Networkscited by 31
  3. 2018Real-to-Virtual Domain Unification for End-to-End Autonomous Drivingcited by 10
  4. 2018Stacked U-Nets: A No-Frills Approach to Natural Image Segmentationcited by 9
  5. 2019Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopycited by 9

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