# Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks

arXiv: https://arxiv.org/abs/2007.03107 · HTML: https://mlsanity.com/paper/2007.03107/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 22 of 22)

- [Efficient-CapsNet: Capsule Network with Self-Attention Routing](https://mlsanity.com/paper/2101.12491/) (2021)
- [Open High-Resolution Satellite Imagery: The WorldStrat Dataset -- With Application to Super-Resolution](https://mlsanity.com/paper/2207.06418/) (2022)
- [Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation](https://mlsanity.com/paper/2111.03231/) (2021)
- [Permutation invariance and uncertainty in multitemporal image super-resolution](https://mlsanity.com/paper/2105.12409/) (2021)
- [MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-Resolution](https://mlsanity.com/paper/2210.02745/) (2022)
- [Image Fusion in Remote Sensing: An Overview and Meta Analysis](https://mlsanity.com/paper/2401.08837/) (2024)
- [Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 images](https://mlsanity.com/paper/2301.11154/) (2023)
- [Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites](https://mlsanity.com/paper/2205.02031/) (2022)
- [Cross-sensor super-resolution of irregularly sampled Sentinel-2 time series](https://mlsanity.com/paper/2404.16409/) (2024)
- [Super-Resolution of BVOC Maps by Adapting Deep Learning Methods](https://mlsanity.com/paper/2302.07570/) (2023)
- [Zooming Out on Zooming In: Advancing Super-Resolution for Remote Sensing](https://mlsanity.com/paper/2311.18082/) (2023)
- [Super-Resolving Beyond Satellite Hardware Using Realistically Degraded Images](https://mlsanity.com/paper/2103.06270/) (2021)
- [Multi-image Super-resolution via Quality Map Associated Attention Network](https://mlsanity.com/paper/2202.13124/) (2022)
- [Super-resolved multi-temporal segmentation with deep permutation-invariant networks](https://mlsanity.com/paper/2204.02631/) (2022)
- [Combination of Single and Multi-frame Image Super-resolution: An Analytical Perspective](https://mlsanity.com/paper/2303.03212/) (2023)
- [CoT-MISR:Marrying Convolution and Transformer for Multi-Image Super-Resolution](https://mlsanity.com/paper/2303.06548/) (2023)
- [ESC-MISR: Enhancing Spatial Correlations for Multi-Image Super-Resolution in Remote Sensing](https://mlsanity.com/paper/2411.04706/) (2024)
- [Super-Resolution for Remote Sensing Imagery via the Coupling of a Variational Model and Deep Learning](https://mlsanity.com/paper/2412.09841/) (2024)
- [Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution](https://mlsanity.com/paper/2501.06720/) (2025)
- [Leveraging Land Cover Priors for Isoprene Emission Super-Resolution](https://mlsanity.com/paper/2503.18658/) (2025)
- [AI-ming backwards: Vanishing archaeological landscapes in Mesopotamia and automatic detection of sites on CORONA imagery](https://mlsanity.com/paper/2507.13420/) (2025)
- [SatFusion: A Unified Framework for Enhancing Remote Sensing Images via Multi-Frame and Multi-Source Images Fusion](https://mlsanity.com/paper/2510.07905/) (2025)

## Abstract (arXiv)

Convolutional Neural Networks (CNNs) have been consistently proved state-of-the-art results in image Super-Resolution (SR), representing an exceptional opportunity for the remote sensing field to extract further information and knowledge from captured data. However, most of the works published in the literature have been focusing on the Single-Image Super-Resolution problem so far. At present, satellite based remote sensing platforms offer huge data availability with high temporal resolution and low spatial resolution. In this context, the presented research proposes a novel residual attention model (RAMS) that efficiently tackles the multi-image super-resolution task, simultaneously exploiting spatial and temporal correlations to combine multiple images. We introduce the mechanism of visual feature attention with 3D convolutions in order to obtain an aware data fusion and information extraction of the multiple low-resolution images, transcending limitations of the local region of convolutional operations. Moreover, having multiple inputs with the same scene, our representation learning network makes extensive use of nestled residual connections to let flow redundant low-frequency signals and focus the computation on more important high-frequency components. Extensive experimentation and evaluations against other available solutions, either for single or multi-image super-resolution, have demonstrated that the proposed deep learning-based solution can be considered state-of-the-art for Multi-Image Super-Resolution for remote sensing applications.