Synthesizing Optical and SAR Imagery From Land Cover Maps and Auxiliary Raster Data
RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Inria（法国国家信息与自动化研究所）；Munich University of Applied Sciences（慕尼黑应用科学大学）；the University of Tokyo（东京大学）
Gerald Baier, Antonin Deschemps, Michael Schmitt, Naoto Yokoya
We synthesize both optical RGB and synthetic aperture radar (SAR) remote sensing images from land cover maps and auxiliary raster data using generative adversarial networks (GANs). In remote sensing, many types of data, such as digital elevation models (DEMs) or precipitation maps, are often not reflected in land cover maps but still influence image content or structure. Including such data in the synthesis process increases the quality of the generated images and exerts more control on their characteristics. Spatially adaptive normalization layers fuse both inputs and are applied to a full-blown generator architecture consisting of encoder and decoder to take full advantage of the information content in the auxiliary raster data. Our method successfully synthesizes medium (10 m) and high (1 m) resolution images when trained with the corresponding data set. We show the advantage of data fusion of land cover maps and auxiliary information using mean intersection over unions (mIoUs), pixel accuracy, and Fréchet inception distances (FIDs) using pretrained U-Net segmentation models. Handpicked images exemplify how fusing information avoids ambiguities in the synthesized images. By slightly editing the input, our method can be used to synthesize realistic changes, i.e., raising the water levels. The source code is available at https://github.com/gbaier/rs_img_synth and we published the newly created high-resolution dataset at https://ieee-dataport.org/open-access/geonrw.
DI3CL: Contrastive Learning With Dynamic Instances and Contour Consistency for SAR Land-Cover Classification Foundation Model
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education（教育部智能感知与图像理解重点实验室）；International Research Center for Intelligent Perception and Computation（智能感知与计算国际研究中心）；Joint International Research Laboratory of Intelligent Perception and Computation（智能感知与计算联合国际实验室）；School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；Hangzhou Institute of Technology（杭州科技学院）
Heterogeneous SAR-optical fusion for near-real-time land use and land cover mapping under cloud contamination: A novel framework and global benchmark dataset
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing（信息工程测绘与遥感国家重点实验室）；School of Computer Science and Information Engineering（计算机科学与信息工程学院）；Hangzhou International Innovation Institute（杭州国际创新研究院）；Oriental Space Port Research Institute（东方航天港研究院）；Hubei Luojia Laboratory（湖北珞珈实验室）
UniDiff: Parameter-Efficient Adaptation of Diffusion Models for Land Cover Classification with Multi-Modal Remotely Sensed Imagery and Sparse Annotations
University of Florence（佛罗伦萨大学）；Centre Tecnològic Telecomunicacions Catalunya（加泰罗尼亚电信技术中心）；University of Padua（帕多瓦大学）
In this letter, we use deep-learning convolution neural networks (CNNs) to assess the landslide mapping and classification performances on optical images (from Sentinel-2) and SAR images (from Sentinel-1). The training and test zones used to independently evaluate the performance of the CNNs on different datasets are located in the eastern Iburi subprefecture in Hokkaido, where, at 03.08 local time (JST) on September 6, 2018, an Mw 6.6 earthquake triggered about 8000 coseismic landslides. We analyzed the conditions before and after the earthquake exploiting multi-polarization SAR as well as op…
Semi-supervised learning techniques are gaining popularity due to their capability of building models that are effective, even when scarce amounts of labeled data are available. In this paper, we present a framework and specific tasks for self-supervised pretraining of \textit{multichannel} models, such as the fusion of multispectral and synthetic aperture radar images. We show that the proposed self-supervised approach is highly effective at learning features that correlate with the labels for land cover classification. This is enabled by an explicit design of pretraining tasks which promotes…
University of California Berkeley（加州大学伯克利分校）；The Ohio State University（俄亥俄州立大学）；German Aerospace Center DLR（德国航空航天中心（DLR））；University of Valencia（瓦伦西亚大学）；National Observatory of Athens（雅典国家天文台）；United Nations Satellite Centre (UNOSAT)（联合国卫星中心（UNOSAT））；University of Oxford（牛津大学）；NVIDIA（英伟达）；Universidad de Antioquia（安蒂奥基亚大学）
Rapid assessment after a natural disaster is key for prioritizing emergency resources. In the case of landslides, rapid assessment involves determining the extent of the area affected and measuring the size and location of individual landslides. Synthetic Aperture Radar (SAR) is an active remote sensing technique that is unaffected by weather conditions. Deep Learning algorithms can be applied to SAR data, but training them requires large labeled datasets. In the case of landslides, these datasets are laborious to produce for segmentation, and often they are not available for the specific regi…
Global Fishing Watch（全球渔业观察）；Cambrio；Defense Innovation Unit（国防创新单元）；UC Berkeley（加州大学伯克利分校）
Unsustainable fishing practices worldwide pose a major threat to marine resources and ecosystems. Identifying vessels that do not show up in conventional monitoring systems -- known as ``dark vessels'' -- is key to managing and securing the health of marine environments. With the rise of satellite-based synthetic aperture radar (SAR) imaging and modern machine learning (ML), it is now possible to automate detection of dark vessels day or night, under all-weather conditions. SAR images, however, require a domain-specific treatment and are not widely accessible to the ML community. Maritime obje…
OpenEarthMap-SAR: A Benchmark Synthetic Aperture Radar Dataset for Global High-Resolution Land Cover Mapping
RIKEN Center for Advanced Intelligence Project (AIP), RIKEN（理化学研究所先进智能中心（AIP），理化学研究所）；Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究生院）
Deep learning based landslide density estimation on SAR data for rapid response
University of California Berkeley（加州大学伯克利分校）；The Ohio State University（俄亥俄州立大学）；German Aerospace Center DLR（德国航空航天中心（DLR））；University of Valencia（瓦伦西亚大学）；National Observatory of Athens（雅典国家天文台）；University of Oxford（牛津大学）；NVIDIA（英伟达）；Universidad de Antioquia（安蒂奥基亚大学）
Synthetic Aperture Radar (SAR) imagery has diverse applications in land and marine surveillance. Unlike electro-optical (EO) systems, these systems are not affected by weather conditions and can be used in the day and night times. With the growing importance of SAR imagery, it would be desirable if models trained on widely available EO datasets can also be used for SAR images. In this work, we consider transfer learning to leverage deep features from a network trained on an EO ships dataset and generate predictions on SAR imagery. Furthermore, by exploring the network activations in the form o…
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…
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Collaborative Innovation Center of Novel Software Technology and Industrialization（新型软件技术与产业化协同创新中心）
This paper studies a practically meaningful ship detection problem from synthetic aperture radar (SAR) images by the neural network. We broadly extract different types of SAR image features and raise the intriguing question that whether these extracted features are beneficial to (1) suppress data variations (e.g., complex land-sea backgrounds, scattered noise) of real-world SAR images, and (2) enhance the features of ships that are small objects and have different aspect (length-width) ratios, therefore resulting in the improvement of ship detection. To answer this question, we propose a SAR-s…
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；German Aerospace Center (DLR)（德国航空航天中心）
The classification of large-scale high-resolution SAR land cover images acquired by satellites is a challenging task, facing several difficulties such as semantic annotation with expertise, changing data characteristics due to varying imaging parameters or regional target area differences, and complex scattering mechanisms being different from optical imaging. Given a large-scale SAR land cover dataset collected from TerraSAR-X images with a hierarchical three-level annotation of 150 categories and comprising more than 100,000 patches, three main challenges in automatically interpreting SAR im…
Fast Fourier Convolutional GAN for 30 m Clear-Sky Land Surface Temperature Gap-Free Reconstruction
Technical University of Munich（慕尼黑工业大学）；Helmholtz Centre for Polar and Marine Research, Alfred Wegener Institute（亥姆霍兹极地与海洋研究中心阿尔弗雷德·韦格纳研究所）；Swiss Federal Institute of Technology Zurich (ETH Zurich)（瑞士联邦理工学院苏黎世分校）
TAR: Text Semantic Assisted Cross-modal Image Registration Framework for Optical and SAR Images
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Artificial Intelligence, Xidian University（中国教育部智能感知与图像理解重点实验室，西安电子科技大学人工智能学院）；Department of Automation, Tsinghua University（清华大学自动化系）
CLAIRE: A Dual Encoder Network with RIFT Loss and Phi-3 Small Language Model Based Interpretability for Cross-Modality Synthetic Aperture Radar and Optical Land Cover Segmentation
Department of Computer Science and Engineering, United International University（计算机科学与工程系，国际大学）；Faculty of Science and Information Technology, Charles Darwin University（科学与信息技术学院，查尔斯·达尔文大学）；School of Engineering and Energy , Murdoch University（工程与能源学院，默多尼大学）；Faculty of Science and Technology, Charles Darwin University（科学与技术学院，查尔斯·达尔文大学）
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR)（德国航空航天中心（DLR）遥感技术研究所（IMF））；Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM)（慕尼黑工业大学（TUM）地球观测信号处理实验室（SiPEO））
Multi-modal data fusion has recently been shown promise in classification tasks in remote sensing. Optical data and radar data, two important yet intrinsically different data sources, are attracting more and more attention for potential data fusion. It is already widely known that, a machine learning based methodology often yields excellent performance. However, the methodology relies on a large training set, which is very expensive to achieve in remote sensing. The semi-supervised manifold alignment (SSMA), a multi-modal data fusion algorithm, has been designed to amplify the impact of an exi…
SARLANG-1M: A Benchmark for Vision-Language Modeling in SAR Image Understanding
Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究生院）；RIKEN Center for Advanced Intelligence Project (AIP), RIKEN（理化学研究所先进智能研究中心）；School of Electronics and Information Engineering, Nanjing University of Information Science and Technology（南京信息工程大学电子与信息工程学院）；The School of Electronic and Communication Engineering, Sun Yat-sen University（中山大学电子与通信工程学院）
High accuracy coastline/shoreline extraction from SAR imagery is a crucial step in a number of maritime and coastal monitoring applications. We present a method based on image segmentation using the Generalised Gamma Mixture Model superpixel algorithm (MISP-GGD). MISP-GGD produces superpixels adhering with great accuracy to object edges in the image, such as the coastline. Unsupervised clustering of the generated superpixels according to textural and radiometric features allows for generation of a land/water mask from which a highly accurate coastline can be extracted. We present results of ou…
NORCE, The Norwegian Research Centre AS（挪威研究中心NORCE）；UiT the Arctic University of Norway（挪威北极大学UiT）
In this paper, we explore the possibility of detecting polar lows in C-band SAR images by means of deep learning. Specifically, we introduce a novel dataset consisting of Sentinel-1 images divided into two classes, representing the presence and absence of a maritime mesocyclone, respectively. The dataset is constructed using the ERA5 dataset as baseline and it consists of 2004 annotated images. To our knowledge, this is the first dataset of its kind to be publicly released. The dataset is used to train a deep learning model to classify the labeled images. Evaluated on an independent test set, …
RSTNet: Enhancing Small-Target Recognition in Noisy SAR Imagery via Robust Feature Learning and Distribution-Aware Regression
College of Mathematics and Information Science, Anyang Institute of Technology（数学与信息科学学院，安阳职业技术学院）；School of Computer Science and Information Engineering, Anyang Institute of Technology（计算机科学与信息工程学院，安阳职业技术学院）；College of Electronic Information and Optical Engineering, Nankai University（电子信息与光学工程学院，南开大学）；Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology（天津光电传感器与传感网络技术重点实验室）；Beijing Xiao Mi Mobile Software Co., Ltd.（北京小米移动软件有限公司）；Anyang Embedded Intelligent Robot Co., Ltd.（安阳嵌入式智能机器人有限公司）
Remote Sensing Technology Institute, German Aerospace Center（德国航空航天中心遥感技术研究所）；Technical University of Munich（慕尼黑工业大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；INRIA（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）
As remote sensing (RS) data obtained from different sensors become available largely and openly, multimodal data processing and analysis techniques have been garnering increasing interest in the RS and geoscience community. However, due to the gap between different modalities in terms of imaging sensors, resolutions, and contents, embedding their complementary information into a consistent, compact, accurate, and discriminative representation, to a great extent, remains challenging. To this end, we propose a shared and specific feature learning (S2FL) model. S2FL is capable of decomposing mult…
The visual inspection of aerial drone footage is an integral part of land search and rescue (SAR) operations today. Since this inspection is a slow, tedious and error-prone job for humans, we propose a novel deep learning algorithm to automate this aerial person detection (APD) task. We experiment with model architecture selection, online data augmentation, transfer learning, image tiling and several other techniques to improve the test performance of our method. We present the novel Aerial Inspection RetinaNet (AIR) algorithm as the combination of these contributions. The AIR detector demonst…
Optical image data have been used by the Remote Sensing workforce to study land use and cover since such data is easily interpretable. Synthetic Aperture Radar (SAR) has the characteristic of obtaining images during all-day, all-weather and provides object information that is different from visible and infrared sensors. However, SAR images have more speckle noise and fewer dimensions. This paper presents a method for denoising, feature extraction and compares classifications of Optical and SAR images. The image was denoised using K-Singular Value Decomposition (K-SVD) algorithm. A method to ma…
Large Scale Masked Autoencoding for Reducing Label Requirements on SAR Data
University of Cambridge（剑桥大学）；Universidad Nacional de Colombia（哥伦比亚国立大学）；Universitat de València（瓦伦西亚大学）；University of Oxford（牛津大学）；Universidad de Antioquia（安蒂奥基亚大学）；European Space Agency（欧洲空间局）
Human civilization has an increasingly powerful influence on the earth system. Affected by climate change and land-use change, natural disasters such as flooding have been increasing in recent years. Earth observations are an invaluable source for assessing and mitigating negative impacts. Detecting changes from Earth observation data is one way to monitor the possible impact. Effective and reliable Change Detection (CD) methods can help in identifying the risk of disaster events at an early stage. In this work, we propose a novel unsupervised CD method on time series Synthetic Aperture Radar~…
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）；School of Information and Electronics Engineering, Beijing Institute of Technology（北京理工大学信息与电子工程学院）；School of Mathematics and Computational Science, Xiangtan University（湘潭大学数学与计算科学学院）
Deep learning methods have made significant progress in ship detection in synthetic aperture radar (SAR) images. The pretraining technique is usually adopted to support deep neural networks-based SAR ship detectors due to the scarce labeled SAR images. However, directly leveraging ImageNet pretraining is hardly to obtain a good ship detector because of different imaging perspective and geometry. In this paper, to resolve the problem of inconsistent imaging perspective between ImageNet and earth observations, we propose an optical ship detector (OSD) pretraining technique, which transfers the c…
Prior-guided Fusion of Multimodal Features for Change Detection from Optical-SAR Images
Institute of Geospatial Information, Information Engineering University（地理信息研究所，信息工程大学）；Academy of Digital China (Fujian), Fuzhou University（数字中国研究院（福建），福州大学）；The School of Electronics and Communication Engineering, Sun Yat-sen University（电子与通信工程学院，中山大学）；The Department of Information Engineering and Computer Science, University of Trento（信息工程与计算机科学系，特伦托大学）
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。
University of Sannio（萨尼奥大学）；Warsaw University of Technology（华沙理工大学）；La Sapienza University（罗马第一大学）；European Space Agency（欧洲空间局）
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 …
SOMA-1M: A Large-Scale SAR-Optical Multi-resolution Alignment Dataset for Multi-Task Remote Sensing
School of Remote Sensing Information Engineering, Wuhan University（遥感信息工程学院，武汉大学）；Hubei LuoJia Laboratory（湖北珞珈实验室）；Technology Innovation Center for Collaborative Applications of Natural Resources Data in GBA, Ministry of Natural Resources（粤港澳大湾区自然资源数据协同应用技术创新中心，自然资源部）
SARLO-80: Worldwide Slant SAR Language Optic Dataset 80cm
DEMR-ONERA – The French Aerospace Lab, Université Paris-Saclay（法国航空航天实验室DEMR-ONERA，巴黎-萨克雷大学）；DTIS-ONERA – The French Aerospace Lab, Université Paris-Saclay（法国航空航天实验室DTIS-ONERA，巴黎-萨克雷大学）；Hugging Face
A Deep Learning Approach to Estimate Canopy Height and Uncertainty by Integrating Seasonal Optical, SAR and Limited GEDI LiDAR Data over Northern Forests
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）
Extending Dynamic World Surface Water Mapping to Sentinel-1 with AlphaEarth Embeddings
Pacific Northwest National Laboratory（太平洋西北国家实验室）；NASA Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心）；Fujitsu Research of America（富士通美国研究院）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
Exploring DINO: Emergent Properties and Limitations for Synthetic Aperture Radar Imagery
Universidad Nacional de Colombia（哥伦比亚国立大学）；European Space Agency（欧洲空间局）；Universitat de València（瓦伦西亚大学）；University of Cambridge（剑桥大学）；University of Oxford（牛津大学）；Universidad de Antioquia（安蒂奥基亚大学）
Improved flood mapping for efficient policy design by fusion of Sentinel-1, Sentinel-2, and Landsat-9 imagery to identify population and infrastructure exposed to floods
Lahore University of Management Sciences (LUMS)（拉合尔管理科学大学（LUMS））
