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 of class-activation maps (CAMs), we visualize the transfer learning process to SAR imagery and gain insight on how a deep network interprets a new modality.
Toshiba Infrastructure Systems & Solutions Corporation（东芝基础设施系统与解决方案公司）
The standard architecture of synthetic aperture radar (SAR) automatic target recognition (ATR) consists of three stages: detection, discrimination, and classification. In recent years, convolutional neural networks (CNNs) for SAR ATR have been proposed, but most of them classify target classes from a target chip extracted from SAR imagery, as a classification for the third stage of SAR ATR. In this report, we propose a novel CNN for end-to-end ATR from SAR imagery. The CNN named verification support network (VersNet) performs all three stages of SAR ATR end-to-end. VersNet inputs a SAR image o…
Toshiba Infrastructure Systems & Solutions Corporation（东芝基础设施系统与解决方案公司）
This report deals with translation invariance of convolutional neural networks (CNNs) for automatic target recognition (ATR) from synthetic aperture radar (SAR) imagery. In particular, the translation invariance of CNNs for SAR ATR represents the robustness against misalignment of target chips extracted from SAR images. To understand the translation invariance of the CNNs, we trained CNNs which classify the target chips from the MSTAR into the ten classes under the condition of with and without data augmentation, and then visualized the translation invariance of the CNNs. According to our resu…
Access to high resolution satellite imagery has dramatically increased in recent years as several new constellations have entered service. High revisit frequencies as well as improved resolution has widened the use cases of satellite imagery to areas such as humanitarian relief and even Search and Rescue (SaR). We propose a novel remote sensing object detection dataset for deep learning assisted SaR. This dataset contains only small objects that have been identified as potential targets as part of a live SaR response. We evaluate the application of popular object detection models to this datas…
Spaceborne synthetic aperture radar can provide meters scale images of the ocean surface roughness day or night in nearly all weather conditions. This makes it a unique asset for many geophysical applications. Sentinel 1 SAR wave mode vignettes have made possible to capture many important oceanic and atmospheric phenomena since 2014. However, considering the amount of data provided, expanding applications requires a strategy to automatically process and extract geophysical parameters. In this study, we propose to apply subaperture decomposition as a preprocessing stage for SAR deep learning mo…
In the training of deep learning models, how the model parameters are initialized greatly affects the model performance, sample efficiency, and convergence speed. Representation learning for model initialization has recently been actively studied in the remote sensing field. In particular, the appearance characteristics of the imagery obtained using the a synthetic aperture radar (SAR) sensor are quite different from those of general electro-optical (EO) images, and thus representation learning is even more important in remote sensing domain. Motivated from contrastive multiview coding, we pro…
State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology（国防科技大学复杂电磁环境效应国家重点实验室）
Aircraft detection in Synthetic Aperture Radar (SAR) imagery is a challenging task in SAR Automatic Target Recognition (SAR ATR) areas due to aircraft's extremely discrete appearance, obvious intraclass variation, small size and serious background's interference. In this paper, a single-shot detector namely Attentional Feature Refinement and Alignment Network (AFRAN) is proposed for detecting aircraft in SAR images with competitive accuracy and speed. Specifically, three significant components including Attention Feature Fusion Module (AFFM), Deformable Lateral Connection Module (DLCM) and Anc…
SARVLM: A Vision Language Foundation Model for Semantic Understanding in SAR Imagery
School of Artificial Intelligence and Robotics, Hunan University（湖南大学人工智能与机器人学院）；Yuelushan Center for Industrial Innovation（岳麓山创新中心）；School of Medical Information Engineering, Jining Medical University（济南医学院医学信息工程学院）
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）
New Mexico Institute of Mining and Technology（新墨西哥矿业技术学院）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Descartes Labs, Inc.（笛卡尔实验室公司）
Anomalous change detection (ACD) methods separate common, uninteresting changes from rare, significant changes in co-registered images collected at different points in time. In this paper we evaluate methods to improve the performance of ACD in detecting human activity in SAR imagery using outdoor music festivals as a target. Our results show that the low dimensionality of SAR data leads to poor performance of ACD when compared to simpler methods such as image differencing, but augmenting the dimensionality of our input feature space by incorporating local spatial information leads to enhanced…
This study presents a new visualization tool for classification of satellite imagery. Visualization of feature space allows exploration of patterns in the image data and insight into the classification process and related uncertainty. Visual Data Mining provides added value to image classifications as the user can be involved in the classification process providing increased confidence in and understanding of the results. In this study, we present a prototype visualization tool for visual data mining (VDM) of satellite imagery. The visualization tool is showcased in a classification study of h…
In this work, we present a methodology for monitoring man-made, construction-like activities in low-resolution SAR imagery. Our source of data is the European Space Agency Sentinel-l satellite which provides global coverage at a 12-day revisit rate. Despite limitations in resolution, our methodology enables us to monitor activity levels (i.e. presence of vehicles, equipment) of a pre-defined location by analyzing the texture of detected SAR imagery. Using an exploratory dataset, we trained a support vector machine (SVM), a random binary forest, and a fully-connected neural network for classifi…
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）
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…
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）
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-bl…
We propose a new convolutional neural network (CNN) which performs coarse and fine segmentation for end-to-end synthetic aperture radar (SAR) automatic target recognition (ATR) system. In recent years, many CNNs for SAR ATR using deep learning have been proposed, but most of them classify target classes from fixed size target chips extracted from SAR imagery. On the other hand, we proposed the CNN which outputs the score of the multiple target classes and a background class for each pixel from the SAR imagery of arbitrary size and multiple targets as fine segmentation. However, it was necessar…
Universidade Federal de Pernambuco（伯南布哥联邦大学）；Universidade Federal do Ceará（塞阿拉联邦大学）
We propose a new four-parameter distribution for modeling synthetic aperture radar (SAR) imagery named the gamma generalized normal (GGN) by combining the gamma and generalized normal distributions. A mathematical characterization of the new distribution is provided by identifying the limit behavior and by calculating the density and moment expansions. The GGN model performance is evaluated on both synthetic and actual data and, for that, maximum likelihood estimation and random number generation are discussed. The proposed distribution is compared with the beta generalized normal distribution…
The Pennsylvania State University（宾夕法尼亚州立大学）；U.S. Army Research Laboratory（美国陆军研究实验室）
Classifying buried and obscured targets of interest from other natural and manmade clutter objects in the scene is an important problem for the U.S. Army. Targets of interest are often represented by signals captured using low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar (SAR) technology. This technology has been used in various applications, including ground penetration and sensing-through-the-wall. However, the technology still faces a significant issues regarding low-resolution SAR imagery in this particular frequency band, low radar cross sections (RCS), small ob…
Automatic Target Recognition (ATR) for military applications is one of the core processes towards enhancing intelligencer and autonomously operating military platforms. Spurred by this and given that Synthetic Aperture Radar (SAR) presents several advantages over its counterpart data domains, this paper surveys and assesses current SAR ATR architectures that employ the most popular dataset for the SAR domain, namely the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. Based on the current methodology trends, we propose a taxonomy for the SAR ATR architectures, along wi…
Pontifical Catholic University of Rio de Janeiro（里约热内卢天主教大学）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（亥姆霍兹德累斯顿罗森多夫研究中心（HZDR））；Technical University of Munich (TUM)（慕尼黑工业大学（TUM））；Getulio Vargas Foundation（热图利奥·瓦加斯基金会）；Institute of Advanced Research in Artificial Intelligence (IARAI)（人工智能高级研究所（IARAI））
The application of deep learning algorithms to Earth observation (EO) in recent years has enabled substantial progress in fields that rely on remotely sensed data. However, given the data scale in EO, creating large datasets with pixel-level annotations by experts is expensive and highly time-consuming. In this context, priors are seen as an attractive way to alleviate the burden of manual labeling when training deep learning methods for EO. For some applications, those priors are readily available. Motivated by the great success of contrastive-learning methods for self-supervised feature repr…
University of California, Santa Barbara（加州大学圣巴巴拉分校）；IBM T.J. Watson Research Center（IBM T.J. 沃森研究中心）
When modeling geo-spatial data, it is critical to capture spatial correlations for achieving high accuracy. Spatial Auto-Regression (SAR) is a common tool used to model such data, where the spatial contiguity matrix (W) encodes the spatial correlations. However, the efficacy of SAR is limited by two factors. First, it depends on the choice of contiguity matrix, which is typically not learnt from data, but instead, is assumed to be known apriori. Second, it assumes that the observations can be explained by linear models. In this paper, we propose a Convolutional Neural Network (CNN) framework t…
Synthetic Aperture Radar (SAR) images are a valuable asset for a wide variety of tasks. In the last few years, many websites have been offering them for free in the form of easy to manage products, favoring their widespread diffusion and research work in the SAR field. The drawback of these opportunities is that such images might be exposed to forgeries and manipulations by malicious users, raising new concerns about their integrity and trustworthiness. Up to now, the multimedia forensics literature has proposed various techniques to localize manipulations in natural photographs, but the integ…
Zhejiang University（浙江大学）；University of Macau（澳门大学）
Displaying the large number of bands in a hyper- spectral image (HSI) on a trichromatic monitor is important for HSI processing and analysis system. The visualized image shall convey as much information as possible from the original HSI and meanwhile facilitate image interpretation. However, most existing methods display HSIs in false color, which contradicts with user experience and expectation. In this paper, we propose a visualization approach based on constrained manifold learning, whose goal is to learn a visualized image that not only preserves the manifold structure of the HSI but also …
Technical University of Munich（慕尼黑工业大学）；German Aerospace Center (DLR)（德国航空航天中心）
Building height retrieval from synthetic aperture radar (SAR) imagery is of great importance for urban applications, yet highly challenging owing to the complexity of SAR data. This paper addresses the issue of building height retrieval in large-scale urban areas from a single TerraSAR-X spotlight or stripmap image. Based on the radar viewing geometry, we propose that this problem can be formulated as a bounding box regression problem and therefore allows for integrating height data from multiple data sources in generating ground truth on a larger scale. We introduce building footprints from g…
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…
FUSAR-GPT : A Spatiotemporal Feature-Embedded and Two-Stage Decoupled Visual Language Model for SAR Imagery
Fudan University（复旦大学）；Discipline and Technology Center of Microwave Vision Intelligent Sensing, Fudan University（微波视觉智能感知学科与技术中心，复旦大学）；Shanghai Innovation Institute（上海创新研究院）
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Synthetic aperture radar (SAR) images are often blurred by phase perturbations induced by uncompensated sensor motion and /or unknown propagation effects caused by turbulent media. To get refocused images, autofocus proves to be useful post-processing technique applied to estimate and compensate the unknown phase errors. However, a severe drawback of the conventional autofocus algorithms is that they are only capable of removing one-dimensional azimuth phase errors (APE). As the resolution becomes finer, residual range cell migration (RCM), which makes the defocus inherently two-dimensional, b…
Unlocking Optical Prior: Spectrum-Guided Knowledge Transfer for SAR Generalized Category Discovery
College of Electronic Science and Technology, National University of Defense Technology（电子科学与技术学院，国防科技大学）；State Key Laboratory of Complex System Simulation and Modeling Technology（复杂系统仿真与建模技术国家重点实验室）
Large-Scale Avalanche Mapping from SAR Images with Deep Learning-based Change Detection
Department of Theoretical and Applied Sciences, University of Insubria（理论与应用科学系，意大利因布里亚大学）；Department of Science and High Technology, University of Insubria（科学与高新技术系，意大利因布里亚大学）
Friedrich-Alexander University Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）
The vast majority of the outlet glaciers and ice streams of the polar ice sheets end in the ocean. Ice mass loss via calving of the glaciers into the ocean has increased over the last few decades. Information on the temporal variability of the calving front position provides fundamental information on the state of the glacier and ice stream, which can be exploited as calibration and validation data to enhance ice dynamics modeling. To identify the calving front position automatically, deep neural network-based semantic segmentation pipelines can be used to delineate the acquired SAR imagery. H…
School of Microelectronics and Communication Engineering, Chongqing University（重庆大学微电子与通信工程学院）；Chongqing Key Laboratory of Space Information Network and Intelligent Information Fusion（重庆市空间信息网络与智能信息融合重点实验室）；Lancaster Environment Centre, Lancaster University（兰卡斯特大学兰卡斯特环境中心）；UK Centre for Ecology & Hydrology（英国生态与水文中心）；Department of Computer Science, Aberystwyth University（阿伯里斯特威斯大学计算机科学系）；Geography and Environmental Science, University of Southampton（南安普顿大学地理与环境科学系）；Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences（中国科学院地理科学与资源研究所）
Small area change detection from synthetic aperture radar (SAR) is a highly challenging task. In this paper, a robust unsupervised approach is proposed for small area change detection from multi-temporal SAR images using deep learning. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchan…
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）
