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检索条件"任意字段=Conference on Algorithms for Synthetic Aperture Radar Imagery IX"

共 871 条记录，以下是61-70 订阅

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Multiple modality sensor fusion from radar, lidar, and electro-optical systems using three-dimensional data representations 29

Multiple modality sensor fusion from radar, lidar, and elect...

引用

conference on algorithms for synthetic aperture radar imagery XXix

作者： Jamora, J. R. Sotirelis, Paul Nolan, Adam Walrath, Jeff Hubbard, Rick Weerasinghe, Rajith Young, Eric Young, Seth Sensors Directorate Air Force Res Lab 2241 Av Circle Wright Patterson AFB OH 45433 USA Etegent Tech LTD 5050 Sect AveSuite 110 Cincinnati OH 45212 USA Univ Alabama Huntsville AL 35899 USA

ISBN: (纸本)9781510650671;9781510650664

Commonly, data exploitation for single sensors utilizes two-dimensional (2D) imagery. To best combine information from multiple sensing modalities, each with their own fundamental differences, we utilize sensor fusion to capture and leverage the inherent weaknesses from different sensing modalities. When fusing multiple sensor modalities together, this approach quickly becomes intractable as each sensor has unique projection planes and resolution. In this work, we present and analyze a data-driven approach for fusing multiple modalities by extracting data representations for each sensor into three-dimensional (3D) space, supporting sensor fusion natively in a common frame of reference. Photogrammetry and computer vision methods for recovering point clouds, such as structure from motion and multi-view stereo, from 2D electro-optical imagery has shown promising results. Additionally, 3D data representations can also be derived from interferometric synthetic aperture radar (IFSAR) and lidar sensors. We use point cloud representations for all three modalities, which allow us to leverage each sensing modality's individual strengths and weaknesses. Given our data-driven focus, we emphasize fusing the point cloud data in controlled scenarios with known parameters. We also conduct an error analysis for each sensor modality based upon sensor position, resolution, and noise.

关键词： Optical radar

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Evaluating active learning methods for synthetic aperture radar maritime object detection 4

Evaluating active learning methods for synthetic aperture ra...

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conference on Artificial Intelligence and Machine Learning in Defense Applications IV

作者： Sato, Jonathan Raheema, Julian Jaszewski, Martin Naval Informat Warfare Ctr Pacific San Diego CA 92152 USA

ISBN: (纸本)9781510655560

Maritime object detection in synthetic aperture radar (SAR) imagery has seen a resurgence of interest due to the introduction of deep object detection models and large-scale datasets from competitions such as xView3. However, as novel examples are seen in the wild and as new SAR sensors emerge, existing models will need to be retrained with relevant new data. Active learning (AL) aims to automate and optimize this data curation process. In this work, we evaluate state-of-the-art AL algorithms for the task of SAR maritime object detection. Through analyzing and identifying gaps in current AL solutions we seek to motivate efforts to improve their utility in practical settings.

关键词： active learning synthetic aperture radar SAR object detection

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A Novel Technique to Remove Noise from SAR Images by Equalizing Neighborhood Surrounding Values and Pixel Smoothing 2

A Novel Technique to Remove Noise from SAR Images by Equaliz...

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2nd International conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Mitra, Baldivya Deshmukh, Maroti Kumar, Abhimanyu Dept. CSE National Institute of Technology Uttarakhand India Dept. CSE National Institute of Technology Patna India

ISBN: (纸本)9798331530389

Automatic Target Recognition (ATR) plays a crucial role in military operations to locate actual targets and response immediately. Target recognition is easier in Standard Operating Conditions (SOC) while it becomes very complex in Extended Operating Conditions (EOC) due to environmental noise, large depression variations, partial occlusion and limited training samples. Deep Learning (DL) has a wide applications in synthetic aperture radar (SAR) imagery. Interpretation of SAR images to recognize actual targets is a major challenge. Researchers from across the world has given different deep learning models for ATR and it is found that most of DL algorithms performs poor in EOC's. We have increased samples of the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset by adding partially occluded targets, different levels of noise and multiple objects in samples of the MSTAR. This paper presents an Object Localization & Deep Learning-based model for ATR. Our model contains a noise removal phase using spatial filtering and feature extraction and also, classification using enhanced VGG-19. The performance of the proposed model is satisfactory. © 2025 IEEE.

关键词： radar imaging

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Development of proto-type KOMPSAT-5 SAR image analysis tool for automatic change detection and cueing 2

Development of proto-type KOMPSAT-5 SAR image analysis tool ...

引用

conference on Microwave Remote Sensing - Data Processing and Applications II

作者： Chae, S. -H. Lee, S. -J. Lee, K. -J. Korea Aerosp Res Inst KARI Daejeon South Korea

ISBN: (纸本)9781510666931;9781510666948

The Republic of Korea is presently operating the KOMPSAT-5 synthetic aperture radar (SAR) satellite and intends to launch the KOMPSAT-6 SAR satellite and a constellation system of micro-SAR satellites in the future. Therefore, the demand for SAR satellite information is increasing in various fields, but many public and private users who use national satellite information in practice use Electro-Optical and Infrared (EO/IR) satellite images. In this research, an independent KOMPSAT-5 SAR imagery analysis tool for Automatic Change Detection and Cueing (ACDC) was developed to promote the utilization of domestic SAR satellite information by practitioners in public sector organizations in Korea. For the development of SAR ACDC, the functions required for each processing step were analyzed in detail, and this was implemented as a proto-type tool. algorithms that are the basis of the KOMPSAT-5 ACDC, for example, KOMPSAT-5 SAR image import, metadata comparison, SAR noise removal filtering, image co-registration, and change image generation, etc. were implemented. Moreover, the proto-type tool was developed so that users can use it correctly and conveniently by supporting an actual user-friendly interface. The ACDC algorithms and the tool are anticipated to be used in the future by users who use Korea's national satellite data for early detection of illegal ships and structures, as well as confirmation of wide-area disaster damage.

关键词： KOMPSAT-5 synthetic aperture radar (SAR) Automatic Change Detection and Cueing (ACDC) SAR image analysis tool

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Quantifying Mangrove Dynamics using Sentinel-1 Multitemporal Data and Random Forest Algorithm 8

Quantifying Mangrove Dynamics using Sentinel-1 Multitemporal...

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8th Asia-Pacific conference on synthetic aperture radar, APSAR 2023

作者： Simarmata, Nirmawana Wikantika, Ketut Darmawan, Soni Harto, Agung Budi Nadzir, Zulfikar Adlan Sari, Dewi Nawang Geodesy and Geomatics Engineering Center for Remote Sensing Institut Teknologi Bandung Institut Teknologi Sumatera Bandung Indonesia Geodesy and Geomatics Engineering Center for Remote Sensing Institut Teknologi Bandung Bandung Indonesia Institute of Geodesy and Geoinformation University of Bonn Geomatics Engineering Institut Teknologi Sumatera Lampung Indonesia Geomatics Engineering Institut Teknologi Sumatera Lampung Indonesia

ISBN: (纸本)9798350393590

Mangroves are significant coastal ecosystems that are susceptible to changes in the environment. In this study, the distribution and size of mangrove expansions were predicted using multitemporal Sentinel-1 synthetic aperture radar (SAR) data and the random forest (RF) method. The data used are multitemporal Sentinel-1 SAR images of 2020, 2021, and 2022 on the coast of South Lampung Regency. We performed pre-processing on these data, including filtration and radiometric correction, to ensure good quality before use in the analysis. SAR features extracted from the multitemporal imagery provide valuable information for identifying and mapping mangroves. The results of Sentinel image extraction explain that vertical-vertical (VV) polarization in 2020 has the lowest value in July with a value of -19.62dB and the highest value in December with a value of -16.48dB;in 2021, the highest value is in November with a value of -16.37dB and the lowest in August with a value of - 19.01dB. In 2022, the lowest value is in February with a value of - 19.87dB and the highest is in May with a value of -16.44dB. Backscatter values in vertical-horizontal (VH) polarization have a relatively similar pattern. These backscatter values are input parameters for RF classification. The classification results are divided into Built-up Area, Road, Water body, sea, mangrove, vegetation, and agriculture classes. The highest accuracy evaluation was obtained in 2021 with a kappa value of 0.93 and an overall accuracy value of 93.3%. The usage of multitemporal Sentinel-1 SAR data and algorithms is significantly aided by this work. ©2023 IEEE.

关键词： synthetic aperture radar

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Harnessing Satellite imagery for Effective Flood Detection using Convolution Neural Network 3

Harnessing Satellite Imagery for Effective Flood Detection u...

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3rd International conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ambarkar, Bhakti Singh, Aman Gupta, Piyush Dhule, Chetankumar Agrawal, Rahul Morris, Nekita Chavhan G H Raisoni College Of Engineering Department Of DIC Nagpur India Department of Data Science G H Raisoni College of Engineering Nagpur India Department of DIC G H Raisoni college of Engineering Nagpur India

ISBN: (纸本)9798331527549

Earth Observation has become essential for monitoring geosciences and human activity. With the increasing availability of data, Artificial Intelligence (AI) algorithms have delivered significant advancements in remote sensing. However, weather-related disasters like hurricanes and floods, often accompanied by dense cloud cover, limit the effectiveness of optical imaging. synthetic aperture radar (SAR), which penetrates clouds, offers a robust alternative. This study presents a novel dataset of co-registered optical and SAR image time series for flood event detection. By integrating spatial and temporal information using a ResNet-GRU architecture, we overcome the challenges posed by limited labeled SAR data. Our method achieves state-of-the-art accuracy, demonstrating resilience in detecting floods under adverse conditions. Performance metrics indicate substantial improvements in disaster response capabilities, emphasizing the practical utility of SAR in flood monitoring. This work provides critical insights for researchers and practitioners in leveraging SAR and optical data for disaster management. © 2025 IEEE.

关键词： radar imaging

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Analysis of Classification algorithms for Oil Spill Recognition Using SAR Data 1

Analysis of Classification Algorithms for Oil Spill Recognit...

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1st International conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Trishika, K. Rakshitha, A. Kodipalli, Ashwini Rao, Trupthi Pushpalatha, V. Rohini, B.R. Global Academy of Technology Dept. of Artificial Intelligence Data Science Bangalore India

ISBN: (纸本)9798350339727

The research work examination extends the application of synthetic aperture radar (SAR) data for recognition of oil let outs. The focus of this examination is on classing approaches for oil let outs detection using SAR imagery. Image processing techniques are used to derive feature vectors for ocean patch. The out-turn of the image processing is a fixed-length column vector for each dubious region used to classify whether the region contain an oil spill or not. The two classes of the data are oil spill and non-spill. The data of this study consists of standard imbalanced machine learning dataset. 937 cases with 48 derived attributes, a patch number, and a class description which has 896 cases as non-oil let out designate with class description 0 and oil spill with 41 cases is indicated by a class description. Basic EDA and data pre-processing are done before model building. Best classifier models are used for prediction of the data. The gradient boosting classifier providing the highest accuracy score of 98% and is selected as the best classifier for the oil prediction. © 2023 IEEE.

关键词： synthetic aperture radar

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Navigating Spatial Insights: Comparative Exploration of Deep Learning algorithms for Flood Detection using Remote Sensing Data 2

Navigating Spatial Insights: Comparative Exploration of Deep...

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2nd IEEE International conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Mittal, Bhavuk Vanzara, Jui Sajidha, S.A. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350360523

Detecting floods, which are highly destructive natural disasters, is crucial for minimizing their impact on both life and infrastructure [1]. Timely identification is essential, and satellites play a key role in assessing the extent of damage and pinpointing areas in urgent need of assistance following events such as floods, earthquakes, or wildfires. The "SEN12-FLOOD"dataset has emerged as a vital resource for flood detection, utilizing both synthetic aperture radar (SAR) and optical imaging. However, a significant challenge lies in the clarity of the images, making it difficult to accurately identify the extent of damage. In response, we propose a comprehensive methodology that incorporates data preprocessing, the comparison of multiple models, and efficient labeling techniques to enhance the accuracy of flood detection. The data preprocessing phase involves the removal of empty folders and irrelevant images, streamlining the dataset for optimal analysis. Additionally, we introduce band stacking, combining satellite imagery bands B2, B3, B4, and B8 to improve image clarity, providing a refined input for flood detection models. The methodology extends to model comparison, wherein multiple models are evaluated to identify the most effective one for accurate flood detection. © 2024 IEEE.

关键词： Satellite imagery

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Uncertainty Analysis of Sea Ice and Open Water Classification on SAR imagery Using a Bayesian CNN 1

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26th International conference on Pattern Recognition, ICPR 2022

作者： Chen, Xinwei Scott, K. Andrea Clausi, David A. University of Waterloo WaterlooONN2L 3G1 Canada

ISBN: (数字)9783031377310

ISBN: (纸本)9783031377303

algorithms designed for ice-water classification of synthetic aperture radar (SAR) sea ice imagery produce only binary (ice and water) output typically using manually labelled samples for assessment. This is limiting because only a small subset of samples are used which, given the non-stationary nature of the ice and water classes, will likely not reflect the full scene. To address this, we implement a binary ice-water classification in a more informative manner taking into account the uncertainty associated with each pixel in the scene. To accomplish this, we have implemented a Bayesian convolutional neural network (CNN) with variational inference to produce both aleatoric (data-based) and epistemic (model-based) uncertainty. This valuable information provides feedback as to regions that have pixels more likely to be misclassified and provides improved scene interpretation. Testing was performed on a set of 21 radarSAT-2 dual-polarization SAR scenes covering a region in the Beaufort Sea captured regularly from April to December. The model is validated by demonstrating;(i) a positive correlation between misclassification rate and model uncertainty and (ii) a higher uncertainty during the melt and freeze-up transition periods, which are more challenging to classify. Integration of the Iterative Region Growing with Semantics (IRGS) unsupervised segmentation algorithm with the Bayesian CNN outputs generates improved classification results, with notable improved results via visual inspection even when sample-based classification rates are similar. © 2023, Springer Nature Switzerland AG.

关键词： synthetic aperture radar

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An Extended Frequency Nonlinear Chirp Scaling Algorithm for High-Speed High-Squint SAR with Curved Trajectory

An Extended Frequency Nonlinear Chirp Scaling Algorithm for ...

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IET International radar conference 2023, IRC 2023

作者： Deng, Kun Huang, Yan Chen, Zhanye Fu, Dongning Chen, Yating Li, Weidong State Key Lab of Millimeter Waves Southeast University Nanjing210096 China Hwa Create Technology Cop. Ltd. Beijing100193 China HWA-NCUT Radar RF Simulation Laboratory Beijing100114 China

ISBN: (纸本)9781839539954

The imagery of High-Speed High-Squint (HSHS) synthetic aperture radar with curved trajectory is a challenging task due to the existence of acceleration, the azimuth-dependent range migration, and azimuth-dependent Doppler parameters. Although existing imaging algorithms can deal with most of high-squint scenarios based on a nonlinear trajectory, these algorithms will be invalid when speed and resolution are increasing. Thus, an extended frequency nonlinear chirp scaling algorithm is developed to handle this issue. First, a more accurate range model is established. Then, a pre-processing is made to eliminate the impact of 3-D acceleration on the Doppler parameters and range-azimuth coupling. Subsequently, a spatial-variant acceleration phase calibration is implemented for range focusing based on bulk range cell migration correction (BRCMC) and extended secondary range compression (SRC). Finally, an extended frequency nonlinear chirp scaling (EFNCS) algorithm is derived for azimuth compression. The effectiveness of the proposed algorithm is verified by computer simulations. © The Institution of Engineering & Technology 2023.

关键词： Chirp modulation

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