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

371 篇 会议
2 篇 期刊文献

馆藏范围

373 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

296 篇 理学
- 284 篇 物理学
- 72 篇 化学
- 12 篇 数学
- 3 篇 海洋科学
- 2 篇 地理学
- 2 篇 地质学
- 1 篇 大气科学
- 1 篇 地球物理学
- 1 篇 统计学（可授理学、...
252 篇 工学
- 158 篇 测绘科学与技术
- 85 篇 电气工程
- 36 篇 仪器科学与技术
- 28 篇 航空宇航科学与技...
- 16 篇 计算机科学与技术...
- 15 篇 软件工程
- 9 篇 信息与通信工程
- 6 篇 光学工程
- 5 篇 控制科学与工程
- 4 篇 土木工程
- 3 篇 机械工程
- 3 篇 电子科学与技术（可...
- 3 篇 建筑学
- 3 篇 化学工程与技术
- 3 篇 环境科学与工程（可...
- 1 篇 船舶与海洋工程
- 1 篇 城乡规划学
203 篇 医学
- 203 篇 临床医学
- 1 篇 特种医学
3 篇 管理学
- 2 篇 图书情报与档案管...
1 篇 农学
- 1 篇 作物学
1 篇 军事学

主题

117 篇 hyperspectral im...
53 篇 hyperspectral
46 篇 target detection
37 篇 remote sensing
36 篇 hyperspectral im...
27 篇 multispectral
26 篇 multispectral im...
23 篇 anomaly detectio...
19 篇 classification
17 篇 sensors
14 篇 multispectral im...
13 篇 image processing
12 篇 dimensionality r...
12 篇 change detection
12 篇 imagery
11 篇 machine learning
10 篇 atmospheric corr...
10 篇 hsi
10 篇 worldview-2
10 篇 detection and tr...

机构

8 篇 los alamos natl ...
8 篇 rochester inst t...
7 篇 rochester inst t...
6 篇 los alamos natl ...
6 篇 mit lincoln lab ...
5 篇 rochester inst t...
5 篇 univ s alabama d...
5 篇 rochester inst t...
4 篇 northeastern uni...
4 篇 spectral sci inc...
4 篇 texas a&m univ d...
4 篇 rochester inst t...
4 篇 univ puerto rico...
3 篇 rochester inst t...
3 篇 los alamos natl ...
3 篇 univ maryland ba...
3 篇 los alamos natl ...
3 篇 telops inc quebe...
3 篇 rochester inst t...
3 篇 univ puerto rico...

作者

19 篇 messinger david ...
19 篇 theiler james
16 篇 velez-reyes migu...
9 篇 vélez-reyes m
8 篇 ziemann amanda
7 篇 du q
6 篇 resmini ronald g...
6 篇 theiler j
5 篇 kaufman jason r.
5 篇 kerekes john
5 篇 chang chein- i
5 篇 cahill nathan d.
4 篇 adler-golden sm
4 篇 meola joseph
4 篇 olsen richard c.
4 篇 ren h
4 篇 chang ci
4 篇 ren christopher ...
4 篇 alam m. s.
4 篇 felde g

语言

373 篇 英文

检索条件"任意字段=Conference on Algorithms for Multispectral and Hyperspectral Imagery"

共 373 条记录，以下是21-30 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：

Deepfaking it: experiments in generative, adversarial multispectral remote sensing 27

Deepfaking it: experiments in generative, adversarial multis...

引用

conference on algorithms, Technologies, and Applications for multispectral and hyperspectral Imaging XXVII

作者： Ren, Christopher X. Ziemann, Amanda Theiler, James Moore, Juston Los Alamos Natl Lab Intelligence & Space Res Div Los Alamos NM 87545 USA Los Alamos Natl Lab Adv Res Cyber Syst Los Alamos NM USA

ISBN: (数字)9781510642928

ISBN: (纸本)9781510642928

In this work we utilize generative adversarial networks (GANs) to synthesize realistic transformations for remote sensing imagery in the multispectral domain. Despite the apparent perceptual realism of the transformed images at a first glance, we show that a deep learning classifier can very easily be trained to differentiate between real and GAN-generated images, likely due to subtle but pervasive artifacts introduced by the GAN during the synthesis process. We also show that a very low-amplitude adversarial attack can easily fool the aforementioned deep learning classifier, although these types of attacks can be partially mitigated via adversarial training. Finally, we explore the features utilized by the classifier to differentiate real images from GAN-generated ones, and how adversarial training causes the classifier to focus on different, lower-frequency features.

关键词： Deep learning Generative Adversarial Networks Machine learning Remote Sensing multispectral imagery

来源：评论

学校读者我要写书评

暂无评论

Reducing false alarms in hyperspectral images using a covariance matrix based on preliminary false detections 27

Reducing false alarms in hyperspectral images using a covari...

引用

conference on algorithms, Technologies, and Applications for multispectral and hyperspectral Imaging XXVII

作者： Ben Shabat, Idan Zinger, Lihi Rotman, Stanley R. Ben Gurion Univ Negev Sch Elect & Comp Engn Beer Sheva Israel

ISBN: (数字)9781510642928

ISBN: (纸本)9781510642928

In image processing, the Matched Filter algorithm uses the estimated covariance matrix to give each pixel a score based on the similarity between the pixel and the signature of the target. While using this target detection algorithm, false alarms are inevitable. In order to solve this problem, a method using an iterative process to produce a second covariance matrix which only uses the most likely false alarms was presented [6]. In this paper, we test this method, attempt to improve it, and expand on the cases in which it is the most effective. In all cases, the new method showed a decrease in false alarms, and in some cases a decrease of over 85%.

关键词： variance covariance matrix False alarms Covariance matrix pixel hyperspectral imagery Iterative Process

来源：评论

学校读者我要写书评

暂无评论

Modeling the Radiative Response of a High Facet Count Rainforest for Synthetic Sensor imagery 27

Modeling the Radiative Response of a High Facet Count Rainfo...

引用

conference on algorithms, Technologies, and Applications for multispectral and hyperspectral Imaging XXVII

作者： Hunter, Robert H. White, Barry C. Patel, Reena R. Ballard, Jerrell R. US Army Engineer Res & Dev Ctr Vicksburg MS 39180 USA

ISBN: (数字)9781510642928

ISBN: (纸本)9781510642928

Computational modeling of spectral and hyperspectral imagery can be performed using radiative flux calculations on highly resolved geometric models. Faceted geometry models are both memory intensive and computationally expensive but allow for a fine-grained approach to radiative modeling. Using high resolution faceted geometry, improved synthetic imagery can be generated from a ray casting sensor model. This paper describes the results of a distributed memory ray tracing architecture for processing high facet count geometry that is capable of modeling radiative flux for highly resolved landscapes. Monte Carlo integration of the radiative transfer equation is coupled with a soil heat transfer model to facilitate solving for temperatures. Ray tracing procedures then use material properties to communicate radiative flux back to a sensor model. Emitted radiation along with mid-wave radiation reflected from neighboring facets and reflected short-wave solar radiation is computed and returned for rays cast from a sensor model. Radiative results of a prototype rainforest have been acquired that demonstrate the modeling capability of the architecture for geometries exceeding 40 million facets. Images of individual spectral components visually validate the legitimacy of the flux simulation. This paper presents an architecture that has been developed with the potential to produce quality synthetic spectral data based on modeling of actual temperature and radiative flux.

关键词： High Performance Computing Synthetic Data multispectral Rendering

来源：评论

学校读者我要写书评

暂无评论

Veritas: an admissible detector for targets of unknown strength 27

<i>Veritas</i>: an admissible detector for targets of unknow...

引用

conference on algorithms, Technologies, and Applications for multispectral and hyperspectral Imaging XXVII

作者： Theiler, James Los Alamos Natl Lab Space Data Sci & Syst Grp Los Alamos NM 87545 USA

ISBN: (数字)9781510642928

ISBN: (纸本)9781510642928

Inspired by the comic faux-Latin aphorism "veritas duo sigma" (truth at two sigmas), an approach to target detection is proposed based on a likelihood ratio test in which the unknown target strength is treated as known, with strength chosen to correspond to a minimal level of detectability. For Gaussian distributions, this strength typically corresponds to two or three sigmas. This detector is admissible, which means that there is no other detector that is uniformly superior to it. The simplicity of the veritas detector permits closed-form solutions to be derived for a variety of signal detection problems. In a series of numerical experiments, these simple detectors are compared to traditional detectors, such as the locally most powerful detector and the generalized likelihood ratio test detector.

关键词： Adaptive matched filter Clutter Clairvoyant fusion Composite hypothesis testing Elliptically-contoured distribution Generalized likelihood ratio test hyperspectral imagery Target detection

来源：评论

学校读者我要写书评

暂无评论

Improving mosquito population models over the Greater Toronto Area using MSI and SAR data 27

Improving mosquito population models over the Greater Toront...

引用

conference on algorithms, Technologies, and Applications for multispectral and hyperspectral Imaging XXVII

作者： DeMets, Sydney A. Ziemann, Amanda Manore, Carrie Russell, Curtis Los Alamos Natl Lab Intelligence & Space Res Div Los Alamos NM 87545 USA Los Alamos Natl Lab Analyt Intelligence & Technol Div Los Alamos NM USA Publ Hlth Ontario Toronto ON Canada

ISBN: (数字)9781510642928

ISBN: (纸本)9781510642928

As West Nile Virus (WNV) and St. Louis Encephalitis (SLE) become more prevalent across North America, there is an increased risk of fatal neuroinvasive cases. In order for public health officials to prepare for these cases and potentially intervene, the ability to forecast mosquito borne disease outbreaks is paramount. In practice, however, such vector borne diseases are notoriously difficult to predict due to their seemingly sporadic spatial and temporal outbreak patterns. Recent research has demonstrated that mosquito abundance is causally related to WNV/SLE prevalence, providing a practical starting point for developing mosquito-borne disease forecasting systems. When focusing on building mosquito population models, understanding the reproduction environment of Culex mosquitos (WNV and SLE's primary vectors) is key: they rely on warmth, water, and vegetation to reproduce. Previous work has shown that global-coverage multispectral imagery (MSI) (i.e., Landsat 8, Sentinel-2) is a valuable resource for characterizing vegetation health as a predictor of mosquito population, but it is limited in that it may not provide the spatial resolution necessary to distinguish between, e.g., a well-fertilized lawn (poor Culex habitat) and a stand of trees (good Culex habitat). The backscatter information collected by synthetic aperture radar (SAR) imagery provides opportunity to distinguish between broader categories of vegetation type, potentially helping to fill this gap. This research uses publicly available global-coverage MSI and SAR imagery (Landsat 8, Sentinel-2, and Sentinel-1) to explore if vegetation type, in tandem with vegetation health, improves our ability to forecast mosquito populations. Vegetation characterization is done over the Greater Toronto Area from 2014 to 2017, and we derive weekly time series from MSI, spectral indices, and SAR for this time period. We then quantify the strength of vegetation health and type as a predictor of Culex abundance.

关键词： multispectral imagery synthetic aperture radar random forest NDVI

来源：评论

学校读者我要写书评

暂无评论

Radiometric assessment of four pan-sharpening algorithms as applied to hyperspectral imagery 26

Radiometric assessment of four pan-sharpening algorithms as ...

引用

conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery XXVI

作者： Ducay, Rey Messinger, David W. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci 54 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (数字)9781510635623

ISBN: (纸本)9781510635623

Pan-sharpening {fusing the spatial and spectral information between panchromatic (PAN) and multispectral (MSI) or hyperspectral (HSI) imagery of a common scene is a hot topic in remote sensing due to a wide range of applications such as target detection, vegetation monitoring, and subsurface detection (e.g. landmines), among others. However, the focus of panchromatic sharpening is generally placed on visual quality of the resulting image and image-wide summary spectral accuracy metrics. Here we are interested in radiometrically accurate panchromatic sharpening of hyperspectral imagery with particular emphasis on spectral algorithm performance. Four pansharpening algorithms are applied to hyperspectral imagery and evaluated for spectral/radiometric fidelity. Two datasets from SHARE2012 were used: one which features rural scene elements and one which features an urban scene. Target detection was also performed to evaluate algorithm sharpening performance. We find that although visually the performance of the four algorithms were roughly similar, they differ in spectral/radiometric fidelity as well as performance in ACE target detection.

关键词： hyperspectral imagery panchromatic sharpening ACE target detection

来源：评论

学校读者我要写书评

暂无评论

Exploring Crop Yield Prediction with Remote Sensing imagery and AI

Exploring Crop Yield Prediction with Remote Sensing Imagery ...

引用

Advances in Computing, Communication and Applied Informatics (ACCAI), International conference on

作者： J Kavipriya G Vadivu Data Science and Business Systems SRM institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Planning for agriculture, allocating resources, and ensuring food security all depend on the accurate and timely estimate of the crop yield. This paper investigates the use of Machine Learning (ML) and Deep Learning methods (DL) to forecast crop yield using remote sensing images. multispectral and hyperspectral images from remote sensing provide a useful source of data for tracking crop health and estimating prospective yield. This study scrutinizes the advantage of the abundant spectral and spatial data that remote sensing sensors have collected. Convolutional neural networks (CNNs) and Machine learning are utilized for feature extraction and prediction, leveraging the hierarchical representations acquired from remote sensing data. A dataset of ground-truth yield data and historical satellite photos are used to train the DL and ML algorithms. In order to improve the model’s generalization to various geographic locations and cropping seasons, transfer learning techniques can also be used. Moreover, interpretable deep learning methods and attention mechanisms are investigated to improve the model’s transparency and reveal key elements that influence yield prediction. By giving farmers, decision-makers, and other stakeholders timely and precise information on crop output estimates, the research’s findings have the potential to greatly advance precision agriculture methods. Deep learning methods combined with satellite imagery show approximately $\mathbf{9 0 \%}$ accuracy for scalable and effective agricultural production monitoring, which will ultimately help sustainable farming methods.

关键词： Deep learning Precision agriculture Satellites Production Predictive models Generative adversarial networks Data models

来源：评论

学校读者我要写书评

暂无评论

Combining multispectral imagery and Synthetic Aperture Radar for Detecting Deforestation 26

Combining Multispectral Imagery and Synthetic Aperture Radar...

引用

conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery XXVI

作者： Reinisch, Elena C. Ziemann, Amanda Flynn, Eric B. Theiler, James Los Alamos Natl Lab Intelligence & Space Res Los Alamos NM 87545 USA

ISBN: (数字)9781510635623

ISBN: (纸本)9781510635623

Forest destruction is a main contributor to carbon emissions and loss of biodiversity, making it a matter of global importance. Due to the large global footprint and often inaccessibility of forested areas, remote sensing is one of the most valuable techniques for monitoring deforestation. Spectral imaging is typically favored for material classification of forested areas and identification of broad swaths of deforestation. However, spectral data can fall short in detecting more subtle destruction beneath the forest canopy. Radar remote sensing can help fill this gap, as it has the ability to penetrate through tree canopies such that pixels capture backscatter information from both the canopy and material beneath it. Synthetic aperture radar in particular can capture this information at fine spatial resolution, and techniques such as polarimetry and interferometry can be used to measure biomass and detect deforestation. In this study, we compare synthetic aperture radar data with multispectral data to improve characterization and identification of source signatures captured within a pixel, with specific consideration to detecting areas where thinning is happening beneath the forest canopy. We focus on identifying different types of forest thinning in the Valles Caldera, located in the Jemez Mountains of northern New Mexico. We apply anomalous change detection to a combination of data products derived from multispectral imagery and synthetic aperture radar to determine which combinations are most effective at identifying anomalous features of interest in thinning regions. We find that comparing phase change measured by synthetic aperture radar interferometry to differenced vegetation indices highlights anomalous relationships in the thinning region. When comparing multispectral reflectance to backscatter intensity measured by synthetic aperture radar, the most successful temporal comparisons contained synthetic aperture radar data during the thinning period. This sug

关键词： synthetic aperture radar multispectral imagery anomalous change detection deforestation

来源：评论

学校读者我要写书评

暂无评论

Deep Snow: Synthesizing Remote Sensing imagery with Generative Adversarial Nets 26

Deep Snow: Synthesizing Remote Sensing Imagery with Generati...

引用

conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery XXVI

作者： Ren, Christopher X. Ziemann, Amanda Theiler, James Durieux, Alice M. S. Los Alamos Natl Lab Intelligence & Space Res Div Los Alamos NM 87545 USA Descartes Labs Inc 100 N Guadalupe St Santa Fe NM USA

ISBN: (数字)9781510635623

ISBN: (纸本)9781510635623

In this work we demonstrate that generative adversarial networks (GANs) can be used to generate realistic pervasive changes in RGB remote sensing imagery, even in an unpaired training setting. We investigate some transformation quality metrics based on deep embedding of the generated and real images which enable visualization and understanding of the training dynamics of the GAN, and provide a useful measure in terms of quantifying how distinguishable the generated images are from real images. We also identify some artifacts introduced by the GAN in the generated images, which are likely to contribute to the differences seen between the real and generated samples in the deep embedding feature space even in cases where the real and generated samples appear perceptually similar.

关键词： Deep Learning Generative Adversarial Networks Machine Learning

来源：评论

学校读者我要写书评

暂无评论

Experiments in Anomalous Change Detection with the Viareggio 2013 Trial Dataset 26

Experiments in Anomalous Change Detection with the Viareggio...

引用

conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery XXVI

作者： Theiler, James Kucer, Michal Ziemann, Amanda Los Alamos Natl Lab Intelligence & Space Res Los Alamos NM 87545 USA Rochester Inst Technol Ctr Imaging Sci Rochester NY 14623 USA

ISBN: (数字)9781510635623

ISBN: (纸本)9781510635623

The "Viareggio 2013 Trial" is a hyperspectral dataset obtained from multiple overflights of the Italian city of Viareggio. Careful management of panels and vehicles in the scene enabled the development of valuable ground truth information. One pair of overflights occurred at different times on the same day, and another pair took place over different days. These data were used to compare and evaluate a variety of automated approaches for discovering anomalous changes. Co-registration of the images is acknowledged to be imprecise, so part of the challenge is to identify anomalous changes in a way that is robust to this misregistration. In particular, we employed a local co-registration adjustment (LCRA) algorithm to ameliorate the effects of misregistration;we employed non-maximal suppression (NMS) to take advantage of the discrete nature of the changes;and we used canonical correlation analysis (CCA) to reduce the dimension of our data. We found that, taken together, these improved the performance of the detectors in the low false alarm rate regime of operation.

关键词： anomalous change detection hyperspectral imagery remote sensing local co-registration adjustment non-maximal suppression canonical correlation analysis elliptically-contoured distribution

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38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：