Synthetic aperture radar (SAR) images are subject to prominent speckle noise, which is generally considered a purely multiplicative noise process. In theory, this multiplicative noise is that the ratio of the standard deviation to the signal value, the "coefficient of variation," is theoretically constant at every point in a SAR image. Most of the filters for speckle reduction are based on this property. Such property is irrelevant for the new filter structure, which is based on directional smoothing (DS) theory, the enhanced directional smoothing (EDS) that removes speckle noise from SAR images without blurring edges. We demonstrate the effectiveness of this new filtering method by comparing it to established speckle noise removal techniques on SAR images.
University of Southern California, Information Sciences Institute（南加州大学信息科学研究所）
Synthetic Aperture Radar (SAR) imaging systems operate by emitting radar signals from a moving object, such as a satellite, towards the target of interest. Reflected radar echoes are received and later used by image formation algorithms to form a SAR image. There is great interest in using SAR images in computer vision tasks such as classification or automatic target recognition. Today, however, SAR applications consist of multiple operations: image formation followed by image processing. In this work, we train a deep neural network that performs both the image formation and image processing t…
University of Moratuwa（莫拉图瓦大学）；Sandia National Laboratories（桑迪亚国家实验室）
Many researches have been carried out for change detection using temporal SAR images. In this paper an algorithm for change detection using SAR videos has been proposed. There are various challenges related to SAR videos such as high level of speckle noise, rotation of SAR image frames of the video around a particular axis due to the circular movement of airborne vehicle, non-uniform back scattering of SAR pulses. Hence conventional change detection algorithms used for optical videos and SAR temporal images cannot be directly utilized for SAR videos. We propose an algorithm which is a combinat…
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）
Airports have an important role in both military and civilian domains. The synthetic aperture radar (SAR) based airport detection has received increasing attention in recent years. However, due to the high cost of SAR imaging and annotation process, there is no publicly available SAR dataset for airport detection. As a result, deep learning methods have not been fully used in airport detection tasks. To provide a benchmark for airport detection research in SAR images, this paper introduces a large-scale SAR Airport Dataset (SAD). In order to adequately reflect the demands of real world applica…
Comparison Study: Glacier Calving Front Delineation in Synthetic Aperture Radar Images With Deep Learning
Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃朗根-纽伦堡大学计算机科学系）；School of Engineering and Design, Technische Universität München（慕尼黑技术大学工程与设计学院）；Institut für Planetare Geodäsie, Technische Universität Dresden（德累斯顿技术大学行星大地测量研究所）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Institut für Geographie, Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃朗根-纽伦堡大学地理研究所）
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…
Ocean University of China（中国海洋大学）；Mississippi State University（密西西比州立大学）；Southwest Jiaotong University（西南交通大学）
Recently, change detection methods for synthetic aperture radar (SAR) images based on convolutional neural networks (CNN) have gained increasing research attention. However, existing CNN-based methods neglect the interactions among multilayer convolutions, and errors involved in the preclassification restrict the network optimization. To this end, we proposed a layer attention-based noise-tolerant network, termed LANTNet. In particular, we design a layer attention module that adaptively weights the feature of different convolution layers. In addition, we design a noise-tolerant loss function t…
Synthetic aperture radar (SAR) images are affected by a spatially-correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation. Despeckling is an important task that aims at removing such noise, so as to improve the accuracy of all downstream image processing tasks. The first despeckling methods date back to the 1970's, and several model-based algorithms have been developed in the subsequent years. The field has received growing attention, sparkled by the availability of powerful deep learning models that have yielded excellent performance for in…
In this paper, we proposed to investigate unsupervised anomaly detection in Synthetic Aperture Radar (SAR) images. Our approach considers anomalies as abnormal patterns that deviate from their surroundings but without any prior knowledge of their characteristics. In the literature, most model-based algorithms face three main issues. First, the speckle noise corrupts the image and potentially leads to numerous false detections. Second, statistical approaches may exhibit deficiencies in modeling spatial correlation in SAR images. Finally, neural networks based on supervised learning approaches a…
Ocean University of China（中国海洋大学）；Mississippi State University（密西西比州立大学）；Southwest Jiaotong University（西南交通大学）
Change detection from synthetic aperture radar (SAR) imagery is a critical yet challenging task. Existing methods mainly focus on feature extraction in spatial domain, and little attention has been paid to frequency domain. Furthermore, in patch-wise feature analysis, some noisy features in the marginal region may be introduced. To tackle the above two challenges, we propose a Dual-Domain Network. Specifically, we take features from the discrete cosine transform domain into consideration and the reshaped DCT coefficients are integrated into the proposed model as the frequency domain branch. Fe…
Ocean University of China（中国海洋大学）；Meteorological Bureau of Tianjin Municipality（天津市气象局）；Mississippi State University（密西西比州立大学）
Synthetic aperture radar (SAR) image change detection is a vital yet challenging task in the field of remote sensing image analysis. Most previous works adopt a self-supervised method which uses pseudo-labeled samples to guide subsequent training and testing. However, deep networks commonly require many high-quality samples for parameter optimization. The noise in pseudo-labels inevitably affects the final change detection performance. To solve the problem, we propose a Graph-based Knowledge Supplement Network (GKSNet). To be more specific, we extract discriminative information from the existi…
A machine learning approach for image classification in synthetic aperture RADAR
Department of Mathematics and Statistics University of Limerick（数学与统计学系利默里克大学）；Health Research Institute (HRI)（健康研究中心）；Department of Computer Science and Information Systems University of Limerick（计算机科学与信息系统系利默里克大学）
Application of Multifractal Analysis to Segmentation of Water Bodies in Optical and Synthetic Aperture Radar Satellite Images
National Scientific and Technical Research Council (CONICET)（国家科学技术研究委员会（CONICET））；Institute of Human Development, National University of General Sarmiento（萨米恩托将军国立大学人类发展研究所）；Faculty of Natural Sciences, National University of La Pampa（拉潘帕国立大学自然科学学院）
University of Hong Kong（香港大学）；Chinese University of Hong Kong（香港中文大学）；Sun Yat-sen University（中山大学）
Although deep learning has achieved great success in image classification tasks, its performance is subject to the quantity and quality of training samples. For classification of polarimetric synthetic aperture radar (PolSAR) images, it is nearly impossible to annotate the images from visual interpretation. Therefore, it is urgent for remote sensing scientists to develop new techniques for PolSAR image classification under the condition of very few training samples. In this letter, we take the advantage of active learning and propose active ensemble deep learning (AEDL) for PolSAR image classi…
Ocean University of China（中国海洋大学）；Mississippi State University（密西西比州立大学）；Southwest Jiaotong University（西南交通大学）
Benefited from the rapid and sustainable development of synthetic aperture radar (SAR) sensors, change detection from SAR images has received increasing attentions over the past few years. Existing unsupervised deep learning-based methods have made great efforts to exploit robust feature representations, but they consume much time to optimize parameters. Besides, these methods use clustering to obtain pseudo-labels for training, and the pseudo-labeled samples often involve errors, which can be considered as "label noise". To address these issues, we propose a Dual Path Denoising Network (DPDNe…
University of Victoria（维多利亚大学）；QB Information Technologies (1QBit)（1QB信息技术公司（1QBit））
The focus of this work is to explore the use of quantum annealing solvers for the problem of phase unwrapping of synthetic aperture radar (SAR) images. Although solutions to this problem exist based on network programming, these techniques do not scale well to larger-sized images. Our approach involves formulating the problem as a quadratic unconstrained binary optimization (QUBO) problem, which can be solved using a quantum annealer. Given that present embodiments of quantum annealers remain limited in the number of qubits they possess, we decompose the problem into a set of subproblems that …
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）
The recognition or understanding of the scenes observed with a SAR system requires a broader range of cues, beyond the spatial context. These encompass but are not limited to: imaging geometry, imaging mode, properties of the Fourier spectrum of the images or the behavior of the polarimetric signatures. In this paper, we propose a change of paradigm for explainability in data science for the case of Synthetic Aperture Radar (SAR) data to ground the explainable AI for SAR. It aims to use explainable data transformations based on well-established models to generate inputs for AI methods, to prov…
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…
In recent years, increasing attentions are paid on object detection in remote sensing imagery. However, traditional optical detection is highly susceptible to illumination and weather anomaly. It is a challenge to effectively utilize the cross-modality information from multi-source remote sensing images, especially from optical and synthetic aperture radar images, to achieve all-day and all-weather detection with high accuracy and speed. Towards this end, a fast multi-source fusion detection framework is proposed in current paper. A novel distance-decay intersection over union is employed to e…
Synthetic Aperture Radar Image Change Detection Based on Global Dynamic Context-Aware Network
State Key Laboratory of Physical Oceanography, Ocean University of China（中国海洋大学物理海洋国家重点实验室）；Department of Electrical and Computer Engineering, Mississippi State University（密苏里州立大学电气与计算机工程系）
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）；University of Valencia（瓦伦西亚大学）；National Observatory of Athens（雅典国家天文台）；United Nations Satellite Centre（联合国卫星中心）；University of Oxford（牛津大学）；NVIDIA（英伟达公司）；Universidad de Antioquia（安蒂奥基亚大学）
With climate change predicted to increase the likelihood of landslide events, there is a growing need for rapid landslide detection technologies that help inform emergency responses. Synthetic Aperture Radar (SAR) is a remote sensing technique that can provide measurements of affected areas independent of weather or lighting conditions. Usage of SAR, however, is hindered by domain knowledge that is necessary for the pre-processing steps and its interpretation requires expert knowledge. We provide simplified, pre-processed, machine-learning ready SAR datacubes for four globally located landslid…
Urban Flood Mapping Using Satellite Synthetic Aperture Radar Data: A Review of Characteristics, Approaches and Datasets
Technical University of Munich（慕尼黑工业大学）；Vienna University of Technology（维也纳工业大学）；ETH Zurich（苏黎世联邦理工学院）；Wuhan University（武汉大学）；DiXplorer AG（4DiXplorer公司）；Luxembourg Institute of Science and Technology (LIST)（卢森堡科学技术研究院）
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）
