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31-40 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：

Too big, too small, or just right? The influence of multispectral image size on mosquito population predictions in the Greater Toronto Area 26

Too big, too small, or just right? The influence of multispe...

引用

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

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)9781510635623

ISBN: (纸本)9781510635623

Outbreaks of West Nile Virus (WNV) and St. Louis Encephalitis (SLE) are projected to increase in frequency and intensity with climate change, underlining the need to develop better mosquito borne disease (MBD) forecasting systems. Spread by Culex, WNV and SLE have seemingly random spatial and temporal outbreaks, making such outbreaks difficult to predict. However, recent studies have found that mosquito abundance and WNV/SLE transmission are strongly correlated, providing researchers with a foundation for the development MBD forecasting systems. Mosquito populations are impacted by several environmental variables, such as humidity, temperature, vegetation, and available breeding habitat. Mosquito-population forecasting models are beginning incorporate spectral data, such as the normalized difference vegetation index (NDVI). Vegetation is a crucial habitat for some mosquito species, and spectral data offers the best estimate of this habitat virtually anywhere on Earth. Additionally, vegetation offers a proxy for understanding how water flows across a landscape, a crucial consideration in urban areas with high landscape heterogeneity. This research explores how the spatial scale (extent) of multispectral imagery used in mosquito population prediction models influences mosquito population forecasts, specifically in the Greater Toronto Area. We derive three monthly time series of standard spectral indices from multispectral imagery over the Greater Toronto Area from 2004 to 2015;each time series is derived from images taken over the same locations, but using images taken over different spatial footprints. We then explore how spectral indices across the three spatial scales perform as predictors for combined Cx. restuans and Cx. pipiens populations.

关键词： multispectral imagery mosquito population forecasting spectral indices NDVI

来源：评论

学校读者我要写书评

暂无评论

hyperspectral analysis: black inks separation in the Gough map of Great Britain 26

Hyperspectral analysis: black inks separation in the Gough m...

引用

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

作者： Huang, Sihan Messinger, David W. Bove, Damien Rochester Inst Technol Chester F Carlson Ctr Imaging Sci 54 Lomb Mem Dr Rochester NY 14623 USA Univ London London England

ISBN: (数字)9781510635623

ISBN: (纸本)9781510635623

The Gough Map is a late medieval map of the island of Great Britain, which has been receiving a lot of research attention and many studies have been conducted to analyze the map. With the hyperspectral image (HSI) of the Gough Map collected, we are allowed to further analysis the map in a different angle. Our goal is to semi-automatically identify and separate iron-gall and carbon black inks written on the Gough map. Unlike traditional target detection problems, there are unique characteristics in our data: Firstly, the targets are not sparsely distributed and their statistical contributions to the background estimation cannot be ignored. Secondly, the spectral differences between the targets are subtle. Lastly, the variances of the background data are so low that the distinction between it and noise is challenging. To address these issues, we made the following modification to the traditional adaptive coherence estimator (ACE): First, manually select the background data instead of using the whole data to estimate the background. Moreover, the pixels with strong spectral features of other pigments are removed and only the black ink candidates are feed into the detector. Thirdly, the number of eigenvectors were limited in the calculation of whitening operator so that the impact of noise can be controlled. It is clear that the trailing eigenvectors are usually dominated by the noise, so the majority of the information of background data can be effectively compressed only with the top-ranked eigenvectors. This is critical because whitening the noise means increasing the variance of the noise based on the fact that our background has super low variances. The whitening matrix needs to be used for whitening target candidates which have subtle spectral differences, and whitening the noise will cause the spectral features to disappear in the whitening sub-space. In conclusion, the whitening operator used in ACE should whiten the useful information of background data and red

关键词： hyperspectral imagery ink identification target detection adaptive coherence estimator(ACE)

来源：评论

学校读者我要写书评

暂无评论

Analysis of spectral data using spatial context 25

Analysis of spectral data using spatial context

引用

25th SPIE conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery

作者： Metcalf, Jeremy P. Olsen, Richard C. Naval Postgrad Sch 833 Dyer Rd Monterey CA 93943 USA

ISBN: (纸本)9781510626386

Spectral LiDAR analysis can be enabled by the use of spatial context, spatial structure, and prior information in the form of map data. LiDAR intensity imagery are analyzed here using an object-based approach which segments the data according to vector information obtained from OpenStreetMap and other vector map information. Polygons and features from the map vectors are used to establish regions of interest for analysis. This automates the training process for use of traditional statistical classifiers and machine learning algorithms. Map-derived objects can demonstrate multiple spectral components which must be resolved to define the primary object, and its semantic label.

关键词： multispectral imagery image segmentation feature extraction Optech Titan GEOBIA LiDAR intensity

来源：评论

学校读者我要写书评

暂无评论

Algorithm development with on-board and ground-based components for hyperspectral gas detection from small satellites 25

Algorithm development with on-board and ground-based compone...

引用

25th SPIE conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery

作者： Theiler, James Love, Steven P. Los Alamos Natl Lab Intelligence & Space Res Div Los Alamos NM 87545 USA

ISBN: (纸本)9781510626386

Los Alamos is currently working toward demonstrating Cubesat-based hyperspectral detection of gas-phase chemical plumes, a goal initially pursued in the internally funded Targeted Atmospheric Chemistry Observations from Space (TACOS) project, and now advancing toward space deployment with the NASA-funded Nanosat Atmospheric Chemistry hyperspectral Observation System (NACHOS). This paper will present a general overview of these projects. Bandwidth considerations prevent full datacube downloads, and so processing algorithms include an on-board processing component to provide matched-filter and RX images for the gases of interest. The downlinked data will additionally include the full spectrum for a small sample of pixels, and one of the challenges for ground-based analysis will be to incorporate these different but incomplete \views" of the datacube into a more physical interpretation/analysis of the downlinked data.

关键词： cubesat satellite hyperspectral imagery algorithm plume detection sparse matrix transform Mahalanobis distance matched filter adaptive coherence estimator

来源：评论

学校读者我要写书评

暂无评论

Multi-sensor anomalous change detection at scale 25

Multi-sensor anomalous change detection at scale

引用

25th SPIE conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery

作者： Ziemann, Amanda Ren, Christopher X. Theiler, James Los Alamos Natl Lab Intelligence & Space Res Los Alamos NM 87545 USA

ISBN: (纸本)9781510626386

Combining multiple satellite remote sensing sources provides a far richer, more frequent view of the earth than that of any single source;the challenge is in distilling these petabytes of heterogeneous sensor imagery into meaningful characterizations of the imaged areas. To meet this challenge requires effective algorithms for combining heterogeneous data to identify subtle but important changes among the intrinsic data variation. The major obstacle to using heterogeneous satellite data to monitor anomalous changes across time is this: subtle but real changes on the ground can be overwhelmed by artifacts that are simply due to the change in modality. Here, we implement a joint-distribution framework for anomalous change detection that can effectively "normalize" for these changes in modality, and does not require any phenomenological resampling of the pixel signal. This flexibility enables the use of satellite imagery from different sensor platforms and modalities. We use multi-year construction of the Los Angeles Stadium at Hollywood Park (in Inglewood, CA) as our testbed, and exploit synthetic aperture radar (SAR) imagery from Sentinel-1 and multispectral imagery from both Sentinel-2 and Landsat 8. We explore results for anomalous change detection between Sentinel-2 and Landsat 8 over time, and also show results for anomalous change detection between Sentinel-1 SAR imagery and Sentinel-2 multispectral imagery.

关键词： change detection anomalous change detection multispectral imagery synthetic aperture radar multi-sensor multi-modal

来源：评论

学校读者我要写书评

暂无评论

Applications of Spectral Image Quality Equation for Longwave Infrared hyperspectral imagery 25

Applications of Spectral Image Quality Equation for Longwave...

引用

25th SPIE conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery

作者： Rankin, Blake M. Broadwater, Joshua B. Jansing, E. David Johns Hopkins Univ Appl Phys Lab Johns Hopkins Rd Laurel MD 20723 USA

ISBN: (纸本)9781510626386

hyperspectral imaging (HSI) technologies span the electro-optical and infrared domains. Longwave infrared (LWIR) HSI is particularly well suited for chemical and material identification in both day and night conditions due to the fact that longwave signals depend on thermal emission and material composition. However, exploitation performance is impacted by spectral data quality, which is driven by fundamental sensor noise characteristics, focal plane array health, spectral and radiometric calibration accuracy, and weather conditions. Previous algorithms have focused on quantifying spectral quality in the visible, near infrared, and shortwave infrared domains. More recently, we developed a spectral image quality equation (SIQE) based on Bayesian Information Criterion (BIC) for quantifying spectral quality of LWIR HSI data. Here, we further develop the algorithm to provide a more intuitive interpretation of the resulting BIC scores by transforming the scores into a metric that more closely resembles target detection scores. In addition to showing how SIQE is correlated with noise-equivalent spectral radiance, we illustrate several applications of SIQE, including the impact of atmospheric/environmental interferences and calibration errors. Our results reveal that SIQE is an effective metric for quantifying hyperspectral data quality, and thus, can be used for filtering data cubes prior to implementing exploitation algorithms.

关键词： hyperspectral imaging longwave infrared spectral image quality

来源：评论

学校读者我要写书评

暂无评论

Object Detection and Classification in Aerial hyperspectral imagery using a Multivariate Hit-or-Miss Transform 25

Object Detection and Classification in Aerial Hyperspectral ...

引用

25th SPIE conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery

作者： Macfarlane, Fraser Murray, Paul Marshall, Stephen White, Henry Univ Strathclyde Dept Elect & Elect Engn Glasgow Lanark Scotland BAE Syst Air Sect Bristol Avon England

ISBN: (纸本)9781510626386

High resolution aerial and satellite borne hyperspectral imagery provides a wealth of information about an imaged scene allowing for many earth observation applications to be investigated. Such applications include geological exploration, soil characterisation, land usage, change monitoring as well as military applications such as anomaly and target detection. While this sheer volume of data provides an invaluable resource, with it comes the curse of dimensionality and the necessity for smart processing techniques as analysing this large quantity of data can be a lengthy and problematic task. In order to aid this analysis dimensionality reduction techniques can be employed to simplify the task by reducing the volume of data and describing it (or most of it) in an alternate way. This work aims to apply this notion of dimensionality reduction based hyperspectral analysis to target detection using a multivariate Percentage Occupancy Hit or Miss Transform that detects objects based on their size shape and spectral properties. We also investigate the effects of noise and distortion and how incorporating these factors in the design of necessary structuring elements allows for a more accurate representation of the desired targets and therefore a more accurate detection. We also compare our method with various other common Target Detection and Anomaly Detection techniques.

关键词： hyperspectral image processing Mathematical morphology Hit-or-Miss Transform Template matching Object detection

来源：评论

学校读者我要写书评

暂无评论

Semi-Supervised Discriminant Feature Selection for hyperspectral imagery Classification 25

Semi-Supervised Discriminant Feature Selection for Hyperspec...

引用

25th SPIE conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery

作者： Dong, Chunhua Naghedolfeizi, Masoud Aberra, Dawit Zeng, Xiangyan Ft Valley State Univ Dept Math & Comp Sci 1005 State Univ Dr Ft Valley GA 31030 USA

ISBN: (纸本)9781510626386

Sparse representation classification (SRC) is being widely applied for target detection in hyperspectral images (HSI). However, due to the problem of the curse of dimensionality and redundant information in HSI, SRC methods fail to achieve high classification performance via a large number of spectral bands. Selecting a subset of predictive features in a high-dimensional space is a challenging problem for hyperspectral image classification. In this paper, we propose a novel discriminant feature selection (DFS) method for hyperspectral image classification in the eigenspace. Firstly, our proposed DFS method selects a subset of discriminant features by solving the combination of spectral and spatial hypergraph Laplacian quadratic problem, which can preserve the intrinsic structure of the unlabeled pixels as well as both the inter-class and intra-class constraints defined on the labeled pixels in the projected low-dimensional eigenspace. Then, in order to further improve the classification performance of SRC, we exploit the well-known simultaneous orthogonal matching pursuit (SOMP) algorithm to obtain the sparse representation of the pixels by incorporating the interpixel correlation within the classical OMP by assuming that neighboring pixels usually consist of similar materials. Finally, the recovered sparse errors are directly used for determining the label of the pixels. The extracted discriminant features are compatibly used in conjunction with the established SRC methods, and can significantly improve their performance for HSI classification. Experiments conducted with the hyperspectral data sets and different experimental settings show that our proposed method increases the classification accuracy and outperforms the state-of-the-art feature selection and classification methods.

关键词： Sparse Representation Image Classification Dimensionality Reduction hyperspectral imagery Feature Selection Laplacian Eigenspace Semi-Supervised Learning

来源：评论

学校读者我要写书评

暂无评论

Geological mapping in Morozumi range and Helliwell hills areas, northern Victoria Land (NVL), Antarctica using remote sensing imagery 40

Geological mapping in Morozumi range and Helliwell hills are...

引用

40th Asian conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019

作者： Pour, Amin Beiranvand Park, Yongcheol Hong, Jong Kuk Muslim, Aidy M. Pradhan, Biswajeet Songdomirae-ro Yeonsu-gu Incheon21990 Korea Republic of Kuala Nerus Terengganu21030 Malaysia School of Systems Management and Leadership Faculty of Engineering and Information Technology University of Technology Sydney NSW Australia

Many regions remain poorly studied in terms of geological mapping in inaccessible regions especially in the Arctic and Antarctica due to harsh conditions and logistical difficulties. Application of specialized image processing techniques is capable of revealing the hidden linearly mixed spectral sources in multispectral and hyperspectral satellite images. In this study, the application of Independent component analysis (ICA) and Constrained Energy Minimization (CEM) algorithms was evaluated for Landsat-8 and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data for geological mapping in Morozumi Range and Helliwell Hills areas, Northern Victoria Land (NVL), Antarctica. ICA algorithm was able to detect hidden linearly mixed spectral sources and low probability target materials in Landsat-8 and ASTER datasets. Fraction images of endmember target minerals such as hematite, goethite, jarosite, alunite, kaolinite, muscovite, epidote, chlorite, calcite, quartz, opal and chalcedony were produced using CEM algorithm for two spatial subsets of ASTER scene covering the Morozumi Range and Helliwell Hills areas. CEM classification image maps indicated that chlorite/hematite, goethite/jarosite/calcite and kaolinite/muscovite are governed in the Morozumi Range and goethite, chlorite, hematite and epidote are most dominated mineral assemblages in the Helliwell Hills area. GPS survey and XRD analysis verified the alteration mineral assemblages detected by ICA and CEM image processing algorithms. The results of this investigation demonstrate the capability of the two algorithms in distinguishing pixel and subpixel targets in the multispectral satellite data. The application of the methods for identifying poorly exposed geologic materials and subpixel exposures of alteration minerals has invaluable implications for geological mapping and mineral exploration in inaccessible regions. © 2020 40th Asian conference on Remote Sensing, ACRS 2019: "Progre

关键词： Landsat

来源：评论

学校读者我要写书评

暂无评论

Optimizing Deep Learning Model Selection for Angular Feature Extraction in Satellite imagery 25

Optimizing Deep Learning Model Selection for Angular Feature...

引用

25th SPIE conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery

作者： Immel, Poppy Desai, Meera Moody, Daniela, I Ursa Space Syst New York NY 14850 USA Barnard Coll New York NY USA

ISBN: (纸本)9781510626386

Deep learning techniques have been leveraged in numerous applications and across different data modalities over the past few decades, more recently in the domain of remotely sensed imagery. Given the complexity and depth of Convolutional Neural Networks (CNNs) architectures, it is difficult to fully evaluate performance, optimize the hyperparameters, and provide robust solutions to a specific machine learning problem that can be easily extended to similar problems, e.g. via transfer learning. Ursa Space Systems Inc. (Ursa) develops novel machine learning approaches to build custom solutions and extract answers from Synthetic Aperture Radar (SAR) satellite data fused with other remote sensing datasets. One application is identifying the orientation with respect to true north of the inlet pipe, which is one common feature located on the top of a cylindrical oil storage tank. In this paper, we propose a two-phase approach for determining this orientation: first an optimized CNN is used to probabilistically determine a coarse orientation of the inlet pipe, followed by a maximum likelihood voting scheme to automatically extract the location of the angular feature within 7.5 degrees. We present a systematic technique to determine the best deep learning CNN architecture for our specific problem and under user-defined optimization and accuracy constraints, by optimizing model hyperparameters (number of layers, size of the input image, and dataset preprocessing) using a manual and grid search approach. The use of this systematic approach for hyperparameter optimization yields increased accuracy for our angular feature extraction algorithm from 86% to 94% and can be extended to similar applications.

关键词： Feature extraction Optimization (mathematics) Binary data Image classification Satellites Performance modeling Satellite imaging Remote sensing Convolutional neural networks

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：