mlsanityData to 14 Oct 2026

Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model

arXiv 2106.12226 ↗First posted 23 Jun 2021cs.CV cs.AI eess.IVcited by 0

Not on the map. Why?

Abstract (arXiv)

Cloud removal is a relevant topic in Remote Sensing as it fosters the usability of high-resolution optical images for Earth monitoring and study. Related techniques have been analyzed for years with a progressively clearer view of the appropriate methods to adopt, from multi-spectral to inpainting methods. Recent applications of deep generative models and sequence-to-sequence-based models have proved their capability to advance the field significantly. Nevertheless, there are still some gaps, mostly related to the amount of cloud coverage, the density and thickness of clouds, and the occurred temporal landscape changes. In this work, we fill some of these gaps by introducing a novel multi-modal method that uses different sources of information, both spatial and temporal, to restore the whole optical scene of interest. The proposed method introduces an innovative deep model, using the outcomes of both temporal-sequence blending and direct translation from Synthetic Aperture Radar (SAR) to optical images to obtain a pixel-wise restoration of the whole scene. The advantage of our approach is demonstrated across a variety of atmospheric conditions tested on a dataset we have generated and made available. Quantitative and qualitative results prove that the proposed method obtains cloud-free images, preserving scene details without resorting to a huge portion of a clean image and coping with landscape changes.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 89 references that are in the map. First posted on arXiv 23 Jun 2021.

Builds on

25 of 89 references.

References · 25

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  3. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  4. 2014Conditional Generative Adversarial Netscited by 2,162
  5. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  6. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
  7. 2019Improved Precision and Recall Metric for Assessing Generative Modelscited by 944
  8. 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 586
  9. 2018Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Networkcited by 420
  10. 2017DualGAN: Unsupervised Dual Learning for Image-to-Image Translationcited by 310
  11. 2020Self-Supervised MultiModal Versatile Networkscited by 276
  12. 2016Coupled Generative Adversarial Networkscited by 216
13 more
  1. 2016Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networkscited by 192
  2. 2020More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classificationcited by 126
  3. 2020FD-GAN: Generative Adversarial Networks with Fusion-discriminator for Single Image Dehazingcited by 80
  4. 2020Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagerycited by 36
  5. 2018Conditional Image-to-Image Translationcited by 30
  6. 2020Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Networkcited by 29
  7. 2017Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Netscited by 26
  8. 2018Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Networkcited by 26
  9. 2020Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translationcited by 11
  10. 2018Multi-temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulationcited by 8
  11. 2020Automatic Dataset Builder for Machine Learning Applications to Satellite Imagerycited by 7
  12. 2021Paradigm selection for Data Fusion of SAR and Multispectral Sentinel data applied to Land-Cover Classificationcited by 4
  13. 2019Single Image Haze Removal Using Conditional Wasserstein Generative Adversarial Networkscited by 3

Built on by

Not cited yet.