Deep Learning for Downscaling Remote Sensing Images: Fusion and super-resolution

Abstract

Over the last decade there has been an increasing frequency and intensity of wildfires across the globe, posing significant threats to human and animal lives, ecosystems, and socio-economic stability. Therefore urgent action is required to mitigate their devastating impact and safeguard Earth's natural resources. Robust Machine Learning methods combined with the abundance of high-resolution satellite imagery can provide accurate and timely mappings of the affected area in order to assess the scale of the event, identify the impacted assets and prioritize and allocate resources effectively for the proper restoration of the damaged region. In this work, we create and introduce a machine-learning ready dataset we name FLOGA (Forest wiLdfire Observations for the Greek Area). This dataset is unique as it comprises of satellite imagery acquired before and after a wildfire event, it contains information from Sentinel-2 and MODIS modalities with variable spatial and spectral resolution, and contains a large number of events where the corresponding burnt area ground truth has been annotated by domain experts. FLOGA covers the wider region of Greece, which is characterized by a Mediterranean landscape and climatic conditions. We use FLOGA to provide a thorough comparison of multiple Machine Learning and Deep Learning algorithms for the automatic extraction of burnt areas, approached as a change detection task. We also compare the results to those obtained using standard specialized spectral indices for burnt area mapping. Finally, we propose a novel Deep Learning model, namely BAM-CD. Our benchmark results demonstrate the efficacy of the proposed technique in the automatic extraction of burnt areas, outperforming all other methods in terms of accuracy and robustness. Our dataset and code are publicly available at: https://github.com/Orion-AI-Lab/FLOGA.

Development and application of a more refined process for extracting rock crack width information based on artificial intelligence

“…Even if the model can obtain a relatively high PSNR value, the actual effect in real scenes is still not satisfactory. However, PSNR is still widely used by researchers due to the lack of more accurate perceptual evaluation indicators [38,39].…”

Towards a Paradigm Shift on Mapping Muddy Waters with Sentinel-2 Using Machine Learning

“…Copernicus satellite missions such as Sentinel-2 and Sentinel-3 can provide both high spatial and temporal resolutions, although not at the same time, since Sentinel-3 comes with almost daily measurements but low spatial resolutions (∼300 m) (https://sentinels.copernicus.eu/ web/sentinel/user-guides/sentinel-3-olci/resolutions/spatial, accessed on 24 August 2023) while Sentinel-2 comes with high spatial but lower temporal resolution. In addition, recent years have shown an emerging interest in the adaptation of deep learning (and especially CNN-based) approaches for the downscaling task by sensor fusion (e.g., [40]). Therefore, we suggest that the monitoring of muddy waters in the context of this work could be improved by the wider adaptation and fusion of Sentinel-2 and Sentinel-3 missions, and the development of deep learning downscaling approaches to generate water quality products of high both temporal and spatial resolution.…”

Super-resolution techniques for Sentinel-5P products

“…Many strategies have been put out over time. [2][3][4] Although interpolation is unable to extract high-frequency information, some interpolators have been proposed to successfully address the SISR problem. 5 More effective approaches exploit image reconstruction, which create a super-resolved image with the same visual features of the low-resolution image but at a higher scale.…”