WV-Net: A foundation model for SAR WV-mode satellite imagery trained using contrastive self-supervised learning on 10 million images
University of Hawai‘i at Mānoa（夏威夷大学马诺阿分校）；University of Washington（华盛顿大学）；University of New Hampshire（新罕布什尔大学）；Univ. Brest（布雷斯特大学）；CNRS（法国国家科学研究中心）；IRD（法国发展研究所）；Ifremer（法国海洋开发研究院）；IUEM（欧洲海洋科学研究所）
Yannik Glaser, Justin E. Stopa, Linnea M. Wolniewicz, Ralph Foster, Doug Vandemark, Alexis Mouche, Bertrand Chapron, Peter Sadowski
The European Space Agency's Copernicus Sentinel-1 (S-1) mission is a constellation of C-band synthetic aperture radar (SAR) satellites that provide unprecedented monitoring of the world's oceans. S-1's wave mode (WV) captures 20x20 km image patches at 5 m pixel resolution and is unaffected by cloud cover or time-of-day. The mission's open data policy has made SAR data easily accessible for a range of applications, but the need for manual image annotations is a bottleneck that hinders the use of machine learning methods. This study uses nearly 10 million WV-mode images and contrastive self-supervised learning to train a semantic embedding model called WV-Net. In multiple downstream tasks, WV-Net outperforms a comparable model that was pre-trained on natural images (ImageNet) with supervised learning. Experiments show improvements for estimating wave height (0.50 vs 0.60 RMSE using linear probing), estimating near-surface air temperature (0.90 vs 0.97 RMSE), and performing multilabel-classification of geophysical and atmospheric phenomena (0.96 vs 0.95 micro-averaged AUROC). WV-Net embeddings are also superior in an unsupervised image-retrieval task and scale better in data-sparse settings. Together, these results demonstrate that WV-Net embeddings can support geophysical research by providing a convenient foundation model for a variety of data analysis and exploration tasks.
Tsinghua University（清华大学）；University of Helsinki（赫尔辛基大学）
Monitoring sustainable development goals requires accurate and timely socioeconomic statistics, while ubiquitous and frequently-updated urban imagery in web like satellite/street view images has emerged as an important source for socioeconomic prediction. Especially, recent studies turn to self-supervised contrastive learning with manually designed similarity metrics for urban imagery representation learning and further socioeconomic prediction, which however suffers from effectiveness and robustness issues. To address such issues, in this paper, we propose a Knowledge-infused Contrastive Lear…
Satellite image analysis has important implications for land use, urbanization, and ecosystem monitoring. Deep learning methods can facilitate the analysis of different satellite modalities, such as electro-optical (EO) and synthetic aperture radar (SAR) imagery, by supporting knowledge transfer between the modalities to compensate for individual shortcomings. Recent progress has shown how distributional alignment of neural network embeddings can produce powerful transfer learning models by employing a sliced Wasserstein distance (SWD) loss. We analyze how this method can be applied to Sentine…
School of Information, University of California, Berkeley（加州大学伯克利分校信息学院）；IBM Chief Analytics Office（IBM首席分析办公室）
Climate change has caused reductions in river runoffs and aquifer recharge resulting in an increasingly unsustainable crop water demand from reduced freshwater availability. Achieving food security while deploying water in a sustainable manner will continue to be a major challenge necessitating careful monitoring and tracking of agricultural water usage. Historically, monitoring water usage has been a slow and expensive manual process with many imperfections and abuses. Ma-chine learning and remote sensing developments have increased the ability to automatically monitor irrigation patterns, bu…
SARMAE: Masked Autoencoder for SAR Representation Learning
School of Information and Electronics, Beijing Institute of Technology, Beijing, China（北京理工大学信息与电子学院，北京，中国）；School of Computer Science, Wuhan University, Wuhan, China（武汉大学计算机学院，武汉，中国）；School of Electronic Information, Wuhan University, Wuhan, China（武汉大学电子信息学院，武汉，中国）；Zhongguancun Academy, Beijing, China（中关村学院，北京，中国）；School of Data Science, Fudan University, Shanghai, China（复旦大学数据科学学院，上海，中国）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China（武汉大学测绘遥感信息工程国家重点实验室，武汉，中国）
A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake
Department of Electrical, Computer and Biomedical Engineering, University of Pavia（帕维亚大学电气、计算机与生物医学工程系）；Department of Engineering, University of Sannio（萨诺尼奥大学工程系）；Italian Space Agency (ASI)（意大利航天局）
Cross-modal Ship Re-Identification via Optical and SAR Imagery: A Novel Dataset and Method
Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间利用技术与工程中心）；University of Chinese Academy of Sciences（中国科学院大学）
University of Florida（佛罗里达大学）；Miami University（迈阿密大学）；Air Force Research Laboratory（空军研究实验室）
Deep learning-based image retrieval has been emphasized in computer vision. Representation embedding extracted by deep neural networks (DNNs) not only aims at containing semantic information of the image, but also can manage large-scale image retrieval tasks. In this work, we propose a deep learning-based image retrieval approach using homography transformation augmented contrastive learning to perform large-scale synthetic aperture radar (SAR) image search tasks. Moreover, we propose a training method for the DNNs induced by contrastive learning that does not require any labeling procedure. T…
Carted；NVIDIA（英伟达）；Frontier Development Lab（前沿发展实验室）；SpaceML
Floods wreak havoc throughout the world, causing billions of dollars in damages, and uprooting communities, ecosystems and economies. The NASA Impact Flood Detection competition tasked participants with predicting flooded pixels after training with synthetic aperture radar (SAR) images in a supervised setting. We propose a semi-supervised learning pseudo-labeling scheme that derives confidence estimates from U-Net ensembles, progressively improving accuracy. Concretely, we use a cyclical approach involving multiple stages (1) training an ensemble model of multiple U-Net architectures with the …
Adaptive Residual Transformation for Enhanced Feature-Based OOD Detection in SAR Imagery
Next Generation Defence Multidisciplinary Technology Research Center, Pohang University of Science and Technology（浦项科技大学下一代国防多学科技术研究中心）；Pohang University of Science and Technology（浦项科技大学）
1 Department of Computer Science, Friendrich-Alexander University Erlangen-Nürnberg, Germany 2 Department of Geography \& Geosciences, Friedrich-Alexander University Erlangen-Nürnberg, Germany
Fluctuations of the glacier calving front have an important influence over the ice flow of whole glacier systems. It is therefore important to precisely monitor the position of the calving front. However, the manual delineation of SAR images is a difficult, laborious and subjective task. Convolutional neural networks have previously shown promising results in automating the glacier segmentation in SAR images, making them desirable for further exploration of their possibilities. In this work, we propose to compute uncertainty and use it in an Uncertainty Optimization regime as a novel two-stage…
Maritime object classification with SAR imagery using quantum kernel methods
Centre for Quantum Information, Simulation and Algorithms（量子信息、模拟与算法中心）；The University of Western Australia（西澳大学）；School of Physics, Mathematics and Computing（物理、数学与计算学院）；Pawsey Supercomputing Centre（帕威中心）
Cross-modal learning for SAR target recognition using optical vision foundation models
School of Engineering, University of Edinburgh（爱丁堡大学工程学院）；Leonardo UK（英国莱昂纳多公司）；School of Engineering and Physical Sciences, Heriot-Watt University（赫瑞瓦特大学工程与物理科学学院）
Beijing University of Chemical Technology（北京化工大学）；Sugon Information Industry Co., Ltd（曙光信息产业有限公司）
The land cover classification has played an important role in remote sensing because it can intelligently identify things in one huge remote sensing image to reduce the work of humans. However, a lot of classification methods are designed based on the pixel feature or limited spatial feature of the remote sensing image, which limits the classification accuracy and universality of their methods. This paper proposed a novel method to take into the information of remote sensing image, i.e., geographic latitude-longitude information. In addition, a dual-branch convolutional neural network (CNN) cl…
Using low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar (SAR) technology for detecting buried and obscured targets, e.g. bomb or mine, has been successfully demonstrated recently. Despite promising recent progress, a significant open challenge is to distinguish obscured targets from other (natural and manmade) clutter sources in the scene. The problem becomes exacerbated in the presence of noisy responses from rough ground surfaces. In this paper, we present three novel sparsity-driven techniques, which not only exploit the subtle features of raw captured data but als…
Toward Near-Real-Time Marine Oil Spill Detection in SAR Imagery using Quantum-Assisted SVM
Division of Computer Science \& Engineering Louisiana State University Baton Rouge, United States；Department of Petroleum Engineeering Louisiana State University Baton Rouge, United States
Flood-DamageSense: Multimodal Mamba with Multitask Learning for Building Flood Damage Assessment using SAR Remote Sensing Imagery
Urban Resilience.AI Lab（城市韧性人工智能实验室）；Zachry Department of Civil and Environmental Engineering（扎克里土木与环境工程系）；Texas A&M University（德克萨斯A&M大学）；College Station, TX（学院站, 德克萨斯）
ETH Zurich（苏黎世联邦理工学院）；Norwegian Institute of Bioeconomy Research（挪威生物经济研究所）；University of Zurich（苏黎世大学）
Monitoring and managing Earth's forests in an informed manner is an important requirement for addressing challenges like biodiversity loss and climate change. While traditional in situ or aerial campaigns for forest assessments provide accurate data for analysis at regional level, scaling them to entire countries and beyond with high temporal resolution is hardly possible. In this work, we propose a method based on deep ensembles that densely estimates forest structure variables at country-scale with 10-meter resolution, using freely available satellite imagery as input. Our method jointly tra…
Predicting butterfly species presence from satellite imagery using soft contrastive regularisation
The Alan Turing Institute, the UK（英国阿尔安·图灵研究所）；Wageningen University & Research, the NL（瓦赫宁根大学与研究中心）；University College London, the UK（伦敦大学学院）；UK Centre for Ecology & Hydrology, the UK（英国生态与水文研究中心）
MVCNet: Multi-View Contrastive Network for Motor Imagery Classification
Ministry of Education Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（教育部图像处理与智能控制重点实验室，人工智能与自动化学院，华中科技大学）；Shenzhen Huazhong University of Science and Technology Research Institute（深圳华中科技大学研究院）
The increasing intensity and frequency of floods is one of the many consequences of our changing climate. In this work, we explore ML techniques that improve the flood detection module of an operational early flood warning system. Our method exploits an unlabelled dataset of paired multi-spectral and Synthetic Aperture Radar (SAR) imagery to reduce the labeling requirements of a purely supervised learning method. Prior works have used unlabelled data by creating weak labels out of them. However, from our experiments we noticed that such a model still ends up learning the label mistakes in thos…
Friedrich-Alexander University Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
Supervised machine learning requires a large amount of labeled data to achieve proper test results. However, generating accurately labeled segmentation maps on remote sensing imagery, including images from synthetic aperture radar (SAR), is tedious and highly subjective. In this work, we propose to alleviate the issue of limited training data by generating synthetic SAR images with the pix2pix algorithm. This algorithm uses conditional Generative Adversarial Networks (cGANs) to generate an artificial image while preserving the structure of the input. In our case, the input is a segmentation ma…
Cloud-Aware SAR Fusion for Enhanced Optical Sensing in Space Missions
Institute of Aerospace and System Engineering, National Taipei University of Technology, Taipei, Taiwan（航空航天工程研究所，台北科技大学，台北，台湾）；Department of Information Technology, Ho Chi Minh City University of Education, Ho Chi Minh City, Vietnam（信息技术系，胡志明市教育大学，胡志明市，越南）
Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology（中国空间技术研究院钱学森空间技术实验室）
Deep learning techniques have made an increasing impact on the field of remote sensing. However, deep neural networks based fusion of multimodal data from different remote sensors with heterogenous characteristics has not been fully explored, due to the lack of availability of big amounts of perfectly aligned multi-sensor image data with diverse scenes of high resolutions, especially for synthetic aperture radar (SAR) data and optical imagery. To promote the development of deep learning based SAR-optical fusion approaches, we release the QXS-SAROPT dataset, which contains 20,000 pairs of SAR-o…
In training machine learning models for land cover semantic segmentation there is a stark contrast between the availability of satellite imagery to be used as inputs and ground truth data to enable supervised learning. While thousands of new satellite images become freely available on a daily basis, getting ground truth data is still very challenging, time consuming and costly. In this paper we present Embedding Earth a self-supervised contrastive pre-training method for leveraging the large availability of satellite imagery to improve performance on downstream dense land cover classification …
Hardware-Aware Deployment of Joint SAR Compression and Despeckling on FPGA
Technical University of Munich（慕尼黑工业大学）；Remote Sensing Technology Institute, German Aerospace Center (DLR)（德国航空航天中心遥感技术研究所）；University of the Bundeswehr Munich（慕尼黑联邦国防军大学）
Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product
University of California - Los Angeles（加利福尼亚大学洛杉矶分校）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Joint Institute for Regional Earth System Science and Engineering, University of California - Los Angeles（加利福尼亚大学洛杉矶分校区域地球系统科学与工程联合研究所）
本文基于 OPERA RTC-S1 数据训练无需标签的自监督 vision transformer，通过逐像素基线分布偏差检测地表扰动，并在三地灾害事件中取得优于现有方法的结果。
