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

3,570 篇 会议
43 篇 期刊文献
8 册 图书

馆藏范围

3,621 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,588 篇 工学
- 1,051 篇 电气工程
- 1,001 篇 计算机科学与技术...
- 916 篇 测绘科学与技术
- 717 篇 信息与通信工程
- 525 篇 软件工程
- 224 篇 光学工程
- 192 篇 仪器科学与技术
- 157 篇 电子科学与技术（可...
- 154 篇 控制科学与工程
- 152 篇 航空宇航科学与技...
- 90 篇 生物工程
- 84 篇 机械工程
- 64 篇 化学工程与技术
- 63 篇 生物医学工程（可授...
- 51 篇 建筑学
- 48 篇 环境科学与工程（可...
- 47 篇 土木工程
1,446 篇 理学
- 1,031 篇 物理学
- 245 篇 数学
- 101 篇 生物学
- 93 篇 系统科学
- 86 篇 化学
- 84 篇 统计学（可授理学、...
- 43 篇 大气科学
- 33 篇 地质学
- 31 篇 地球物理学
686 篇 医学
- 684 篇 临床医学
174 篇 管理学
- 97 篇 图书情报与档案管...
- 81 篇 管理科学与工程(可...
39 篇 农学
- 31 篇 作物学
15 篇 军事学
12 篇 经济学
11 篇 法学
5 篇 文学
3 篇 教育学
2 篇 艺术学

主题

1,090 篇 remote sensing
209 篇 image processing
176 篇 feature extracti...
173 篇 synthetic apertu...
170 篇 image segmentati...
152 篇 satellites
140 篇 signal processin...
118 篇 image resolution
111 篇 image reconstruc...
110 篇 image classifica...
108 篇 hyperspectral im...
105 篇 image fusion
101 篇 deep learning
97 篇 image analysis
91 篇 spatial resoluti...
89 篇 radar imaging
89 篇 sensors
87 篇 image coding
87 篇 signal processin...
71 篇 classification

机构

27 篇 wuhan univ sch r...
20 篇 school of remote...
20 篇 beijing key labo...
19 篇 natl aerosp univ...
17 篇 institute of rem...
17 篇 beijing normal u...
15 篇 univ maryland ba...
14 篇 beijing institut...
13 篇 beijing institut...
12 篇 beijing key labo...
12 篇 beijing institut...
11 篇 univ chinese aca...
11 篇 school of inform...
11 篇 beijing key lab ...
11 篇 chinese acad sci...
11 篇 university of ch...
11 篇 univ maryland de...
10 篇 univ maryland ba...
9 篇 beijing inst spa...
9 篇 school of artifi...

作者

36 篇 bruzzone lorenzo
32 篇 chang chein-i
29 篇 zhang libao
24 篇 chehdi kacem
17 篇 vozel benoit
16 篇 bovolo francesca
15 篇 lukin vladimir v...
14 篇 chen zhenzhong
14 篇 chanussot jocely...
14 篇 chang chein- i
13 篇 farah imed riadh
12 篇 chen shih-yu
12 篇 chen liang
11 篇 middelmann wolfg...
11 篇 zhang haopeng
11 篇 benediktsson jon...
10 篇 yuan yuan
9 篇 abramov sergey k...
9 篇 li yao
9 篇 datcu mihai

语言

3,449 篇 英文
131 篇 其他
31 篇 土耳其文
9 篇 中文
2 篇 俄文

检索条件"任意字段=Conference on Image and Signal Processing for Remote Sensing IX"

共 3621 条记录，以下是731-740 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：

ENHANCED HYPERSPECTRAL image SUPER-RESOLUTION VIA RGB FUSION AND TV-TV MINIMIZATION

ENHANCED HYPERSPECTRAL IMAGE SUPER-RESOLUTION VIA RGB FUSION...

引用

IEEE International conference on image processing (ICIP)

作者： Vella, Marija Zhang, Bowen Chen, Wei Mota, Joao F. C. Heriot Watt Univ Inst Sensors Signals & Syst Edinburgh EH14 4AS Midlothian Scotland Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China

ISBN: (纸本)9781665441155

Hyperspectral (HS) images contain detailed spectral information that has proven crucial in applications like remote sensing, surveillance, and astronomy. However, because of hardware limitations of HS cameras, the captured images have low spatial resolution. To improve them, the low-resolution hyperspectral images are fused with conventional high-resolution RGB images via a technique known as fusion based HS image super-resolution. Currently, the best performance in this task is achieved by deep learning (DL) methods. Such methods, however, cannot guarantee that the input measurements are satisfied in the recovered image, since the learned parameters by the network are applied to every test image. Conversely, model-based algorithms can typically guarantee such measurement consistency. Inspired by these observations, we propose a framework that integrates learning and model based methods. Experimental results show that our method produces images of superior spatial and spectral resolution compared to the current leading methods, whether model- or DL-based.

关键词： Deep learning super-resolution hyper-spectral imaging optimization total variation

来源：评论

学校读者我要写书评

暂无评论

A fast back-projection algorithm based on equivalent slant range model for high-squint SAR imaging

A fast back-projection algorithm based on equivalent slant r...

引用

IET International Radar conference (IRC 2023)

作者： GaoTian Xu Tianyi Ca Yachao Li Tinghao Zhang National Key Laboratory of Radar Signal Processing Xidian University Xi’an People's Republic of China Beijing Institute of Remote-Sensing Equipment Beijing People's Republic of China

ISBN: (数字)9781837240982

Because back-projection algorithm (BPA) is independent of the azimuth-invariant hypothesis of the echo signal, it has significant advantages for synthetic aperture radar (SAR) imaging with arbitrary trajectory. Although Cartesian fast back-projection algorithm (CFBPA) is proposed to improve the efficiency of BPA, for high-squint SAR (HS-SAR) imaging, the geometric configuration of HS-SAR greatly enhances the space-variant property of the spectrum, which leads to the ineffectively compressed spectrum and decreases the efficiency of the sub-image merging in CFBPA processing. In this paper, an equivalent slant range model (ESRM) based on hybrid coordinate system (HCS) is proposed for HS-SAR imaging. Benefiting from the designed ESRM, the space-variant property of the wavenumber spectrum can be significantly degraded to improve performance in spectrum compression and accelerates the sub-image merging. Promising results from simulation experiments are provided to validate the high performance of the proposed algorithm.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Curvelet Transform based Denoising of Multispectral remote sensing images 1

Curvelet Transform based Denoising of Multispectral Remote S...

引用

1st International conference on Applied Mathematics, Modeling and Simulation in Engineering, AMSE 2021

作者： Reddy, P. Lokeshwara Pawar, Santosh Reddy, S.L. Prathapa Department of ECE Dr. A.P.J. Abdul Kalam University Madhya Pradesh Indore India Department of ECE K.S.R.M. College of Engineering Andhra Pradesh Kadapa India

With the advent of sensor technology, the exertion of multispectral image (MSI) is comely omnipresent. Denoising is an essential quest in multispectral image processing which further improves recital of unmixing, classification and supplementary ensuing praxis. Explication and ocular analysis are essential to extricate data from remote sensing images for broad realm of supplications. This paper describes curvelet transform based denoising of multispectral remote sensing images. The implementation of curvelet transform is done by using both wrapping function and unequally spaced fast Fourier transform (USFFT) and they diverge in selection of spatial grid which is used to construe curvelets at every orientation and scale. The coefficients of curvelets are docket by a scaling factor, angle and spatial location criterion. This paper crisps on denoising of Linear Imaging Self Scanning Sensor (LISS) III images. The proposed denoising approach has also been collated with some existing schemes for assessment. The efficacy of proposed approach is analyzed with calculation of facet matrices such as Peak signal to noise ratio and Structural similarity at distinct variance of noise.. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

SAR Target Zero-Shot Recognition with Optical image Assistance

SAR Target Zero-Shot Recognition with Optical Image Assistan...

引用

signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Jian Chen Qifeng Yong Lan Du Yashi Zhou Jie Liu National Key Laboratory of Radar Signal Processing Xidian University Xi’an China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Most synthetic aperture radar (SAR) target recognition (ATR) methods focus on closed set conditions, which assumes that the testing set shares the same label space with the training set. However, new targets inevitably appear in practical testing phase. Closed-set SAR target recognition methods will misclassify those unseen new targets as the known targets in training phase, causing decision errors. Zero-shot recognition (ZSR) is an effective way to make predictions for unknown classes. Existing ZSR methods, which are primarily designed for optical images, typically construct an attribute knowledge base shared among known and unknown targets, and then learn the feature-attribute mapping using only known class data, which is subsequently applied to predict attributes of unknowns during testing and achieve class judgment. Unfortunately, the optical ZSR methods do not perform well in SAR data, due to the huge difficulty in the learning of feature-attribute mapping caused by the less accurate target representation of SAR images than the optical images. To overcome this problem, this paper proposes a ZSR method of SAR target with optical image assistance. In detail, on the basis of attribute knowledge base construction, the proposed method maps the known optical and SAR training data to the feature space through their respective feature extraction network, and uses feature aggregation loss to ensure that optical and SAR image features from the same category are close to each other, while those from different categories remain far apart. Based on the alignment of optical and SAR features, the feature-attribute mapping is learned using both SAR and optical data, leveraging the advantages of optical images in the easier and more accurate learning of feature-attribute mapping to promote the learning of SAR images. In testing phase, the learned SAR feature extraction network and feature-attribute mapping are used to predict unknown targets’ attributes. Ultimately, the unknown

关键词： Integrated optics image recognition Accuracy Target recognition Knowledge based systems Optical imaging Feature extraction Adaptive optics Synthetic aperture radar Testing

来源：评论

学校读者我要写书评

暂无评论

BISVP: Building Footprint Extraction Via Bidirectional Serialized Vertex Prediction

BISVP: Building Footprint Extraction Via Bidirectional Seria...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Mingming Zhang Ye Du Zhenghui Hu Qingjie Liu Yunhong Wang State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Hangzhou Innovation Institute Beihang University Zhongguancun Laboratory Beijing China

Extracting building footprints from remote sensing images has been attracting extensive attention recently. Dominant approaches address this challenging problem by generating vectorized building masks with cumbersome refinement stages, which limits the application of such methods. In this paper, we introduce a new refinement-free and end-to-end building footprint extraction method, which is conceptually intuitive, simple, and effective. Our method, termed as BiSVP, represents a building instance with ordered vertices and formulates the building footprint extraction as predicting the serialized vertices directly in a bidirectional fashion. Moreover, we propose a cross-scale feature fusion (CSFF) module to facilitate high resolution and rich semantic feature learning, which is essential for the dense building vertex prediction task. Without bells and whistles, our BiSVP outperforms state-of-the-art methods by considerable margins on three building instance segmentation benchmarks, clearly demonstrating its superiority. The code and datasets will be made public available.

关键词： Representation learning image segmentation Fuses Buildings Semantics signal processing Feature extraction

来源：评论

学校读者我要写书评

暂无评论

A Strong Scattering Solution with Applications to remote sensing Systems Modelling

A Strong Scattering Solution with Applications to Remote Sen...

引用

Irish signals and Systems conference (ISSC)

作者： Jonathan Blackledge School of Electrical and Electronic Engineering Technological University Dublin Dublin Ireland

Although not always fully appreciated as such, the linear stationary convolution model for a signal and/or image, generated by the interaction of an incident wave field with an inhomogeneous medium, is based on the ‘weak scattering condition’, commonly referred to as the ‘Born approximation’. This approximation forms the basis for signal processing and analysis associated with applications over a broad range of frequencies. The Born approximation assumes that a scattering interaction is based on single scattering events alone and that the scattered field is therefore a weak field in comparison to the incident field. The physical limitations of such a model are usually overcome by introducing an additional stochastic field term which takes into account all effects that do not conform to the Born approximation, coupled with background ‘system noise’. In this paper, a solution to the ‘scattering problem’ for scalar fields is presented, which does not explicitly rely on the weak scattering condition. It is shown how, for side-band systems, this solution can be cast in terms of a linear stationary model, thereby allowing standard signal analysis and processing methods to be adopted. A case study is presented based on the application of a self-affine model for the scattering function to simulate Synthetic Aperture Radar images.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Generative Adversarial Networks for Synthesizing InSAR Patches 13

Generative Adversarial Networks for Synthesizing InSAR Patch...

引用

13th European conference on Synthetic Aperture Radar (EUSAR)

作者： Sibler, Philipp Wang, Yuanyuan Auer, Stefan Ali, Syed Mohsin Zhu, Xiao Xiang Hensoldt Sensors GmbH D-88090 Immenstaad Germany German Aerosp Ctr DLR Remote Sensing Technol Inst IMF D-82234 Wessling Germany Tech Univ Munich TUM Signal Proc Earth Observat D-80333 Munich Germany

ISBN: (纸本)9783800754571

Generative Adversarial Networks (GANs) have been employed with certain success for image translation tasks between optical and real-valued SAR intensity imagery. Applications include aiding interpretability of SAR scenes with their optical counterparts by artificial patch generation and automatic SAR-optical scene matching. The synthesis of artificial complex-valued InSAR image stacks asks for, besides good perceptual quality, more stringent quality metrics like phase noise and phase coherence. This paper provides a signal processing model of generative CNN structures, describes effects influencing those quality metrics and presents a mapping scheme of complex-valued data to given CNN structures based on popular Deep Learning frameworks.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Hyperspectral Methods in Microscopy image Analysis: A Survey 18

Hyperspectral Methods in Microscopy Image Analysis: A Survey

引用

18th International conference on signal processing and Multimedia Applications (SIGMAP)

作者： Nasr-Esfahani, Shirin Muthukumar, Venkatesan Regentova, Emma E. Taghva, Kazem Trabia, Mohamed B. Univ Nevada Dept Comp Sci Las Vegas NV 89154 USA Univ Nevada Dept Elect & Comp Engn Las Vegas NV 89154 USA Univ Nevada Dept Mech Engn Las Vegas NV 89154 USA

ISBN: (纸本)9789897585258

Hyperspectral imaging (HSI) has found applications in remote sensing, agriculture, medicine, and biology. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Hyperspectral microscope imaging (HMI) is an emerging imaging spectroscopy technology, which combines the advantages of HSI with microscopic imaging;HSI provides rapid, nondestructive, and chemical free data analysis, whereas a microscope can be used to study microstructure of a sample such as nanoparticles. Integration of HSI and microscopy, results in nondestructive evaluation using both spatial and spectral information along with analysis at the molecular or cellular level. The aim of the survey is an overview of the recent applications for HMI in medicine and biology fields.

关键词： Biology Confocal Dark-field Fluorescence Hyperspectral Microscope Imaging (HMI) Medicine

来源：评论

学校读者我要写书评

暂无评论

An improved IHS fusion method of GF-2 remote sensing images

An improved IHS fusion method of GF-2 remote sensing images

引用

2021 International conference on signal image processing and Communication, ICSIPC 2021

作者： Yi, Wei Zeng, Yong Wang, Yuhao Deng, Jianan Su, Wenbo Yuan, Zheng China Center for Resources Satellite Data and Application Beijing100094 China

ISBN: (数字)9781510645417

ISBN: (纸本)9781510645400

The IHS transform fusion is one of the most widely used techniques for image fusion. However, the IHS transform fusion brings spectral distortion. In order to develop new image fusion methods, it is necessary to investigate the spectral features of the original images from different sensors. In this study, high-resolution panchromatic images were reconstructed to improve IHS transform based on GF-2 satellite images. The NSCT transform was used in order to separate details and spectral information. A synthetic index (SI) for assessing fidelity was proposed with consideration of average gradient, entropy, correlation coefficient and spectral distortion. Results show that, in urban areas, the SI of improved IHS method increases from 2.75 to 4.30, and the SI of the hybrid method (improved IHS + NSCT method) increases from 6.68 to 6.93. In addition, the proposed method helps to improve the SI from 1.10 to 3.80 and the NSCT from 6.00 to 7.46 for vegetation covered areas. Thus, the improved IHS transform would maintain spectral fidelity and significantly improve the vegetation spectral information. © 2021 SPIE.

关键词： image fusion

来源：评论

学校读者我要写书评

暂无评论

Classification of Hyperspectral images Using 3D CNN Based ResNet50 29

Classification of Hyperspectral Images Using 3D CNN Based Re...

引用

29th IEEE conference on signal processing and Communications Applications (SIU)

作者： Firat, Huseyin Hanbay, Davut Dicle Univ Bilgisayar Teknol Bolumu Tekn Bilimler MYO Diyarbakir Turkey

ISBN: (纸本)9781665436496

Hyperspectral images are images containing rich spectral and spatial information widely used in remote sensing applications. The development of deep learning techniques has had a significant impact on the classification of hyperspectral images. Different Convolutional Neural Network architectures have been used in many hyperspectral image analysis studies. However, the high dimensions of the hyperspectral images increased the computational complexity. For this reason, dimensionality reduction has been used in the preprocessing stage in many studies. Another difficulty encountered in hyperspectral image classification studies is the need to consider both spectral and spatial features. When deep spatial and spectral features are to be extracted, problems such as loss of gradient properties and degradation due to increased depth arise. In this study, the 3D convolutional neural network (CNN) based ResNet50 method is proposed to solve these problems encountered in hyperspectral studies and to extract sufficient spatial spectral properties from the network. Principal Component Analysis (PCA) was used to reduce spectral band excess. The proposed method has been applied to Pavia University and Salinas data sets. Overall accuracy, average accuracy and kappa values were used to measure the performance of the method. Calculated overall accuracy, average accuracy, and kappa values are 99.99% for the Pavia University data set, and while the overall accuracy and kappa values were 99.99% for the Salinas data set, the average accuracy value was 99.98%.

关键词： 3D CNN ResNet50 Hyperspectral image Classification Principal Component Analysis Deep Learning

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363页 << < 70 71 72 73 74 75 76 77 78 79 > >>

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：