检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

862 篇 会议
8 篇 期刊文献

馆藏范围

870 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

626 篇 工学
- 293 篇 信息与通信工程
- 239 篇 电气工程
- 211 篇 测绘科学与技术
- 202 篇 计算机科学与技术...
- 56 篇 仪器科学与技术
- 50 篇 软件工程
- 35 篇 电子科学与技术（可...
- 33 篇 航空宇航科学与技...
- 26 篇 控制科学与工程
- 22 篇 光学工程
- 9 篇 土木工程
- 8 篇 建筑学
- 7 篇 力学（可授工学、理...
- 7 篇 化学工程与技术
- 7 篇 环境科学与工程（可...
- 6 篇 动力工程及工程热...
- 6 篇 水利工程
- 6 篇 生物工程
443 篇 理学
- 393 篇 物理学
- 48 篇 数学
- 15 篇 统计学（可授理学、...
- 12 篇 化学
- 9 篇 海洋科学
- 7 篇 生物学
- 7 篇 系统科学
- 6 篇 大气科学
270 篇 医学
- 270 篇 临床医学
17 篇 管理学
- 9 篇 管理科学与工程(可...
- 9 篇 图书情报与档案管...
9 篇 农学
- 6 篇 作物学
2 篇 军事学
1 篇 法学

主题

465 篇 synthetic apertu...
101 篇 sar
100 篇 radar imaging
94 篇 radar
58 篇 synthetic apertu...
55 篇 automatic target...
49 篇 image processing
43 篇 image resolution
41 篇 detection and tr...
34 篇 target detection
33 篇 target recogniti...
31 篇 feature extracti...
28 篇 sensors
27 篇 radar polarimetr...
26 篇 remote sensing
24 篇 image segmentati...
22 篇 imaging
21 篇 speckle
20 篇 machine learning
20 篇 signal processin...

机构

12 篇 ohio state univ ...
12 篇 wright state uni...
12 篇 rensselaer polyt...
11 篇 univ florida dep...
10 篇 wright state uni...
9 篇 air force res la...
8 篇 air force res la...
8 篇 res lab adelphi ...
7 篇 air force res la...
6 篇 sandia natl labs...
6 篇 mit lincoln lab ...
5 篇 univ michigan an...
5 篇 univ massachuset...
5 篇 ohio state univ ...
5 篇 air force res la...
5 篇 naval postgrad s...
4 篇 utah state univ ...
4 篇 rensselaer polyt...
4 篇 duke univ dept e...
4 篇 mit informat & d...

作者

16 篇 yazici birsen
16 篇 zelnio edmund
13 篇 rigling brian d.
13 篇 pepin matthew
12 篇 nguyen l
11 篇 zelnio edmund g.
11 篇 majumder uttam
11 篇 garren david a.
9 篇 li j
9 篇 waldman j
9 篇 nixon we
9 篇 lewis benjamin
9 篇 majumder uttam k...
8 篇 wang ling
8 篇 minardi michael ...
7 篇 dickinson jc
7 篇 ranney k
7 篇 goyette tm
7 篇 minardi michael
7 篇 potter lee c.

语言

863 篇 英文
5 篇 其他
2 篇 中文

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

共 870 条记录，以下是41-50 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

AROMA Autofocus of SAR Moving Targets with Complicated Heading Trajectories 29

AROMA Autofocus of SAR Moving Targets with Complicated Headi...

引用

conference on algorithms for synthetic aperture radar imagery XXix

作者： Garren, David A. Naval Postgrad Sch Elect & Comp Engn Monterey CA 93943 USA

ISBN: (纸本)9781510650671;9781510650664

Moving targets are often smeared in synthetic aperture radar (SAR) imagery beyond recognition. Recent analysis has yielded an ability to perform Arbitrary Rigid Object Motion Autofocus (AROMA) in order to perform automatic refocus of targets which exhibit arbitrary temporal profiles of target translation and rotation during the SAR collection interval. This investigation examines the efficacy of AROMA for targets with complex heading profiles amidst a background of measured Ku-band SAR imagery. It is found that AROMA is able to generate well focused imagery which corresponds with the underlying true structure of the target scattering shapes.

关键词： synthetic aperture radar radar imaging moving target imaging

来源：评论

学校读者我要写书评

暂无评论

Episodic processing of SAR scenes over varied wide apertures 29

Episodic processing of SAR scenes over varied wide apertures

引用

conference on algorithms for synthetic aperture radar imagery XXix

作者： Rigling, Brian D. Wright State Univ Dayton OH 45435 USA

ISBN: (纸本)9781510650671;9781510650664

An algorithm for episodic processing of SAR data over wide angles is extended to accommodate larger image formats, beyond the image chips demonstrated previously. Episodic processing of SAR imagery over wide angles permits exploration of RF scattering behavior that is observable over extended apertures. These behaviors include sparsity, persistence, scintillation, and angular dependence. Experiments with larger measured scenes are presented, and we demonstrate the impact of varied angular width for the measured aperture data.

关键词： synthetic aperture radar radar scattering

来源：评论

学校读者我要写书评

暂无评论

Multi-perspective SAR to 3D Translation using Generative AI

Multi-perspective SAR to 3D Translation using Generative AI

引用

IEEE radar conference (radarConf)

作者： Newey, Michael Kuczynski, James Laher, Rebecca Chan, Michael Vasile, Alexandru MIT Lincoln Lab Lexington MA 02421 USA

ISBN: (纸本)9798350329216;9798350329209

This work explores the use of generative adversarial networks (GAN) for multi-look SAR to 3D conversion. We extend 2D-to-2D image translation techniques such as CycleGAN to convert SAR imagery to 3D, taking advantage of existing LiDAR data to provide the 3D information for model training. We use collected X-band radar data from the MITLL ARTB sensor, LiDAR from the MITLL AOSTB sensor and USGS public data in our experiments. We evaluate GAN-based translation performance on large sub-urban scenes as well as on small chips centered on ground vehicles. We evaluate the performance of the algorithms with different number and extents of synthetic aperture radar look angles. Finally, for the case of under- or non-represented cases in training data, we introduce a novel inverted simulation augmentation training-and-test procedure for target classification.

关键词： synthetic aperture radar Artificial Neural Networks LiDAR

来源：评论

学校读者我要写书评

暂无评论

Performance Envelope of 3D Spotlight SAR across Different Data Models, Flight Profiles and Imaging 30

Performance Envelope of 3D Spotlight SAR across Different Da...

引用

conference on algorithms for synthetic aperture radar imagery XXX

作者： Pepin, Matthew USAF Albuquerque NM 87123 USA

ISBN: (纸本)9781510661547;9781510661554

The achievable performance of synthetic aperture radar (SAR) localization and imaging is limited ultimately by that of the noise-free phase history data acquisition and imaging. Noise and motion errors (not investigated here) can only degrade from this performance. The range of parameters corresponding to good performance constitute an envelope of good performance. Point Scatterer location, accuracy, resolution and focus (IPR or Impulse Response) vary across the parameters of frequency, bandwidth, stand-off range, number of spatial (array) and temporal samples, and linear or circular flight path and their squint angle from broadside. Practical point source localization uses an iterative process to identify point source volume cell (voxel) centers and precisely locating these within the voxel by applying a damped exponential estimate of the ideal point source. Measurements between each dimension of a grid of point sources determines any distortion of the 3D image volume and IPR determines any change in focus over the grid points in the volume. IPR measurements are made with a search for the half-amplitude point of the main lobe width of the Point Scatterer response in each dimension. The performance of SAR parameters is measured for a spherical array model of level circular flight paths and their linearized alternatives. The phase history data are modeled with both single and multiple direct rays reflecting from idealized point sources or corner reflector as well as a spherical differential range model. Performance differences due to different combinations of phase history data collection and imaging techniques are cataloged.

关键词： synthetic aperture radar 3D image processing 3D acquisition Computer aided design 3D modeling Image resolution Solid modeling Simulations

来源：评论

学校读者我要写书评

暂无评论

Improving Oil Spill Detection in Marine Environments Through Deep Learning Approaches 4

Improving Oil Spill Detection in Marine Environments Through...

引用

4th International conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Shanmukh, M. Phani Priya, S. Baghavathi Madeswaran, Tamilselvi Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India University of Technology and Applied Sciences Department of Information Technology Nizwa Oman

ISBN: (纸本)9798350343670

Oil spills represent a major environmental threat, particularly in marine ecosystems. Timely detection and segmentation of oil spills from satellite imagery is crucial for directing rapid response and remediation *** research concludes with an insights by developing a deep learning approach for automated detection and segmentation of oil spills from synthetic aperture radar (SAR) *** research implements and compares three algorithms PSPNET,DeepLapV3 and fully convolutional networks (FCN) with U-Net for semantic segmentation of SAR *** the networks are trained on a dataset of SAR images with pixel-wise labels indicating oil spill regions. This research also evaluates the segmentation performance using common metrics like intersection-over-union (IoU) and pixel-wise accuracy. Our results indicate that both FCN and U-Net can effectively detect and delineate oil spill regions, with U-Net demonstrating slightly higher IoU scores and with and accuracy of 95%. The deep learning models generalize well to unseen SAR images. Overall, this work demonstrates the feasibility of deep convolutional neural networks for automated analysis of SAR imagery to enable rapid oil spill response at marine environment. © 2024 IEEE.

关键词： Deep Learning algorithms Oil Spill Detection Rapid Oil Spill Response Semantic Segmentation synthetic aperture radar (SAR) imagery

来源：评论

学校读者我要写书评

暂无评论

Analysis of Autofocus algorithms in Near-Range Wide-Angle UAV-SAR Imaging

Analysis of Autofocus Algorithms in Near-Range Wide-Angle UA...

引用

2023 Kleinheubach conference, KHB 2023

作者： Brucker, Philipp Bischeltsrieder, Florian Dill, Stephan Engel, Marius Jungbauer, Florian Peichl, Markus Jelonnek, John Weßling Germany Karlsruhe Germany

ISBN: (纸本)9783948571085

Rapid developments in Unmanned Aerial Vehicles and highly integrated radar systems allow for innovative synthetic aperture radar concepts in close-range high-resolution applications. However, the stringent position accuracy requirements of radar imaging pose a challenge for state-of-the-art position sensors. While data-driven correction approaches have been used successfully in airborne systems, they are rarely in use in close-range scenarios. This paper presents a theoretical analysis of the effects of antenna position inaccuracies on close-range radar imagery. Based on a theoretical approach, an upper bound for the performance of phase-only autofocus algorithms is derived. The applicability of two recently published algorithms in near-range SAR is shown using a Monte Carlo simulation framework and demonstrated on experimental data-sets. © 2023 URSI Landesausschuss in Deutschland e.V.

关键词： synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Neural Network and Likelihood-Ratio Approach to Multichannel SAR Change Detection 76

A Hybrid Neural Network and Likelihood-Ratio Approach to Mul...

引用

76th Annual IEEE National Aerospace and Electronics conference (NAECON)

作者： Herman, Stephen Ash, Joshua Wright State Univ Dept Elect Engn Dayton OH 45435 USA

ISBN: (纸本)9798350367638;9798350367621

Exploitation algorithms for synthetic aperture radar (SAR) frequently rely on the Gaussian backscatter assumption. However, previous research has shown that this assumption breaks down for high resolution SAR, and non-Gaussian models such as the generalized Gamma (GG) more effectively represent this type of imagery. In particular, this model has shown advantages when applied in non-coherent change detection. Unlike Gaussian-based change detection algorithms where analytical methods are easily applied for parameter estimation, in the GG model, time intensive numerical optimization is required, limiting the practical utility of these performant algorithms. In this paper, we propose a hybrid neural network and likelihood-ratio based approach to SAR change detection that addresses this problem. We consider the unique identifiability challenges associated with the GG model whereby particular distributions may be closely approximated by widely varying generative parameters. This precludes the use of traditional parametric mean squared-error metrics when fitting the GG model to SAR scenes. Instead, we propose the use of the information theoretic Kullback-Leibler divergence as a loss metric while training a neural network to efficiently model backscatter behavior using the GG model. We demonstrate efficacy of the approach through a simulation study and quantify significant improvement in inference speed relative to previous optimization-based maximum likelihood approaches. Finally, we apply the proposed estimator to measured polarimetric SAR data and show that the hybrid approach, despite having been trained entirely using synthetic data, performs well when applied to non-coherent change detection.

关键词： synthetic aperture radar change detection deep learning

来源：评论

学校读者我要写书评

暂无评论

Eigenimage-based synthetic aperture radar ATR 29

Eigenimage-based Synthetic Aperture Radar ATR

引用

conference on algorithms for synthetic aperture radar imagery XXix

作者： Lewis, Benjamin Garrett, Jonathan Sensors Directorate Air Force Res Lab Wright Patterson AFB OH 45433 USA Wright State Univ Dayton OH 45435 USA

ISBN: (纸本)9781510650671;9781510650664

synthetic aperture radar is an all-weather sensor with many uses, including target recognition. We present our latest efforts to train a network on synthetic SAR imagery for good performance on measured images. We apply an eigenimage-based classification network to the SAMPLE dataset, a dataset of synthetic and measured SAR imagery. Eigenimages are extracted from the synthetic images, then used to encode both types of images. This encoding takes the form of a vector describing the weighted contribution of each eigenimage to a given image. This reduces the extraneous noise in the measured image and helps bridge the gap between the two domains. We train a variety of networks, including fully-connected, support vector machines, and logistic regression, on the weight vectors for synthetic images, then test on measured vectors. We present the results on the publicly available SAMPLE dataset.

关键词： synthetic aperture radar automatic target recognition machine learning principal components

来源：评论

学校读者我要写书评

暂无评论

CNN-based false alarm mitigation for maritime objects detected in low resolution synthetic aperture radar imagery 30

CNN-based false alarm mitigation for maritime objects detect...

引用

conference on algorithms for synthetic aperture radar imagery XXX

作者： Biron, Katerina Def Res & Dev Canada DRDC Ottawa Res Ctr ORC 3701 Carling Ave Ottawa ON Canada

ISBN: (纸本)9781510661547;9781510661554

Canada's radarSAT-2 (R-2) low-resolution ScanSAR Detection of Vessels, Wide swath, Far incidence angle (DVWF) mode was implemented for vessel detection over wide swaths. Between 2013 and 2020, DVWF imagery was used operationally, by the Department of National Defence (DND), and exploited using a tool that detects clusters of bright pixels with vessel-like signature in the SAR imagery. The detected objects were then associated with automatic identification system (AIS) messages. Not all vessels transmit AIS messages, and not all detected vessels that transmit AIS are associated;therefore, analysts observed non-associated objects in an attempt to validate detected vessels. The new work described here evaluates the use of a convolutional neural network (CNN) that detects and rejects false alarms to assist analysts with this validation. A CNN is first calibrated with 1,963 DVWF images containing 16,490 detected and AIS associated vessels (referred to as SAR detected and AIS associated (SAR-AIS) vessels) and 5,654 known false alarms mostly off the coasts of Canada. The CNN's usability is then evaluated, globally, with 94,562 DVWF images containing 209,746 SAR-AIS vessels, 203,559 validated vessels, and 261,499 unknown objects, detected operationally and non-operationally. During calibration, the CNN classified SAR-AIS vessels and false alarms with 91.6% and 96.3% accuracy, respectively. When evaluating usability, the CNN correctly classified 93.7% of the 209,746 SAR-AIS vessels. False alarms were determined for 5.4% of the 203,559 validated vessels, and 58.1% of the 261,499 unknown objects. These results suggest that a CNN designed to detect and reject false alarms could reduce the number of objects requiring validation by approximately 30%.

关键词： SAR big data CNN maritime

来源：评论

学校读者我要写书评

暂无评论

Characterizing SAR image exploitation as a function of operating conditions 29

Characterizing SAR image exploitation as a function of opera...

引用

conference on algorithms for synthetic aperture radar imagery XXix

作者： Scherreik, Matthew Nolan, Adam Stephens, Shaun Westerkamp, Lori Zelnio, Edmund Air Force Res Lab Wright Patterson AFB OH 45433 USA Etegent Technol Norwood MA USA

ISBN: (纸本)9781510650671;9781510650664

We propose methods to improve the use of synthetic data for characterizing SAR image exploitation in terms of various operating conditions (OCs). More specifically, we describe tools that simulate statistically relevant samples of SAR imagery and classify the resulting imagery via a baseline algorithm. The associated OC generation, user interfaces, databasing of OCs, classification, analysis, and visualization have been containerized and ported to run on the DoD Supercomputing Resource Centers. To demonstrate our workflow, we present four case studies with the quantized grayscale matching algorithm. The work described here provides a foundation to support future developments in multi-look and multi-sensor fusion.

关键词： synthetic aperture radar automatic target recognition operating conditions

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共87页 << < 1 2 3 4 5 6 7 8 9 10 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：