Preprint Technical Note Version 1 Preserved in Portico This version is not peer-reviewed

Satellite Image Multi-Frame Super-Resolution Using 3D Wide-Activation Neural Networks

Version 1 : Received: 24 September 2020 / Approved: 27 September 2020 / Online: 27 September 2020 (11:54:56 CEST)

How to cite: Dorr, F. Satellite Image Multi-Frame Super-Resolution Using 3D Wide-Activation Neural Networks. Preprints 2020, 2020090678 (doi: 10.20944/preprints202009.0678.v1). Dorr, F. Satellite Image Multi-Frame Super-Resolution Using 3D Wide-Activation Neural Networks. Preprints 2020, 2020090678 (doi: 10.20944/preprints202009.0678.v1).

Abstract

The small satellite market continues to grow year after year. A compound annual growth rate of 17% is estimated during the period between 2020 and 2025. Low-cost satellites can send a vast amount of images to be post-processed at the ground to improve the quality and extract detailed information. In this domain lies the resolution enhancement task, where a low-resolution image is converted to a higher resolution automatically. Deep learning approaches to Super-Resolution (SR) reached the state-of-the-art in multiple benchmarks; however, most of them were studied in a single-frame fashion. With satellite imagery, multi-frame images can be obtained at different conditions giving the possibility to add more information per image and improve the final analysis. In this context, we developed and applied to the PROBA-V dataset of multi-frame satellite images a model that recently topped the European Space Agency’s Multi-frame Super Resolution (MFSR) competition. The model is based on proven methods that worked on 2D images tweaked to work on 3D: the Wide Activation Super Resolution (WDSR) family. We show that with a simple 3D CNN residual architecture with WDSR blocks and a frame permutation technique as data augmentation better scores can be achieved than with more complex models. Moreover, the model requires few hardware resources, both for training and evaluation, so it can be applied directly from a personal laptop.

Subject Areas

multi-frame super resolution; wide activation super resolution; 3D convolutional neural network; deep learning

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.