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

3,569 篇 会议
37 篇 期刊文献
8 册 图书

馆藏范围

3,614 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,581 篇 工学
- 1,048 篇 电气工程
- 1,000 篇 计算机科学与技术...
- 917 篇 测绘科学与技术
- 710 篇 信息与通信工程
- 525 篇 软件工程
- 224 篇 光学工程
- 193 篇 仪器科学与技术
- 157 篇 电子科学与技术（可...
- 155 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
- 49 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
687 篇 医学
- 685 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...
40 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

1,088 篇 remote sensing
208 篇 image processing
176 篇 feature extracti...
169 篇 image segmentati...
168 篇 synthetic apertu...
154 篇 satellites
140 篇 signal processin...
118 篇 image resolution
112 篇 image reconstruc...
112 篇 hyperspectral im...
109 篇 image classifica...
105 篇 image fusion
101 篇 deep learning
97 篇 image analysis
91 篇 spatial resoluti...
91 篇 sensors
87 篇 signal processin...
87 篇 radar imaging
86 篇 image coding
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

3,522 篇 英文
51 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 3614 条记录，以下是261-270 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：

Research on Snapshot GISC Hyperspectral image Analysis Technology Based on Mean Filtering Algorithm 2

Research on Snapshot GISC Hyperspectral Image Analysis Techn...

引用

2nd International conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Liu, Zhuo Li, Xinyang Li, Meixuan Changchun College of Electronic Technology Changchun China Jilin Engineering Normal University Changchun China

ISBN: (纸本)9798350386639

With the improvement of the quality of life, people pay more and more attention to the quality of diet, and fruits and vegetables are an important part of a healthy diet, as well as essential nutrients for people to maintain physical health. Therefore, this paper proposes a non-destructive detection method for fruit and vegetable quality-GISC hyperspectral imaging technology. The principle of GISC hyperspectral imaging technology is introduced, and the mean filtering method is used to denoise the characteristic signal data periodically and at high frequencies. The mean and variance of the denoised signal data are calculated to form a feature vector. Finally, the effectiveness of the scheme is verified by using information entropy for image quality evaluation. © 2024 IEEE.

关键词： Wiener filtering

来源：评论

学校读者我要写书评

暂无评论

image Fusion Technique Using Singular Value Decomposition Based Undecimated Subband Decomposition

Image Fusion Technique Using Singular Value Decomposition Ba...

引用

2023 IEEE International conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Evers, Julia Fraunhofer Iis Fraunhofer Institute for Integrated Circuits Iis Erlangen Germany

ISBN: (纸本)9798350327816

image fusion is an important area of research. In remote sensing, the usage of the same sensor in different working modes, or different image sensors, can provide complementary information. Therefore, it is needed to fuse outputs from multiple sensors (or the same sensor in different working modes) to improve the performance of the remote images, which are very useful for many image processing tasks. Singular Value Decomposition Undecimated Subband Decomposition (SVD-USBD) of images with its Multi-Resolution schemes and based on it novel image fusion technique have been presented. The performance of this algorithm is compared with that of often used image fusion technique using Multi-Resolution form of Singular Value Decomposition (MR-SVD). It is observed that image fusion by SVD-USBD provides almost similar fusion quality and that is why it can be used in combinations with other methods to develop hybrid schemes for multi-focus image fusion or fusion of different types of images as infrared or termal imaging. © 2023 IEEE.

关键词： image sub-band decomposition multi-resolution singular value decomposition Multi-sensor image fusion SVD

来源：评论

学校读者我要写书评

暂无评论

Multispectral image Fusion based on Super Pixel Segmentation 48

Multispectral Image Fusion based on Super Pixel Segmentation

引用

48th IEEE International conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Ofir, Nati Kingston University London United Kingdom

ISBN: (纸本)9781728163277

Multispectral image fusion is a computer vision process that is essential to remote sensing. For applications such as dehazing and object detection, there is a need to offer solutions that can perform in real-time on any type of scene. Unfortunately, current state-of-the-art approaches do not meet these criteria as they need to be trained on domain-specific data and have high computational complexity. This paper focuses on the task of fusing color (RGB) and near-infrared (NIR) images as this the typical RGBT sensors, as in multispectral cameras for detection, fusion, and dehazing. Indeed, the NIR channel has the ability to capture details not visible in RGB and see beyond haze, fog, and clouds. To combine this information, a novel approach based on superpixel segmentation is designed so that multispectral image fusion is performed according to the specific local content of the images to be fused. There-fore, the proposed method produces a fusion that contains the most relevant content of each spectrum. The experiments re-ported in this manuscript show that the novel approach better preserves details than alternative fusion methods. © 2023 IEEE.

关键词： image Fusion Multispectral images Near-Infrared Superpixel Segmentation

来源：评论

学校读者我要写书评

暂无评论

7th International conference on Vision, image and signal processing, ICVISP 2023

7th International Conference on Vision, Image and Signal Pro...

引用

7th International conference on Vision, image and signal processing, ICVISP 2023

ISBN: (纸本)9781839539848

The proceedings contain 47 papers. The topics discussed include: a modified UNet network bridged with multiscale context fusion for photovoltaic panel image segmentation;a new method for radar emitter individual identification based on VMD and multi-image feature combination;research on axle bearing fault detection method based on multilayer feature fusion under sparse training samples;diffusion posterior sampling for remote sensing image fusion;mutual reconstruction-based linear discriminant hashing for image retrieval;joint face super-resolution and deblurring using multi-task feature fusion network;AmpNorm: an effective style normalization method for single domain generalization;construction of marine target detection dataset and target detection methods;and deep learning sea surface object detection method based on infrared image.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Airplane symmetry-based Convolutional Neural Network for airplane detection in remote sensing image

Airplane symmetry-based Convolutional Neural Network for air...

引用

IET International Radar conference 2023, IRC 2023

作者： Cheng, Bei Wang, Bo Liu, Zao Wang, Qingwang Shen, Tao Gu, Yebo Kunming University of Science and Technology No. 72 7 Jingming South Road Chenggong District Yunnan Kunming City China School of Information Engineering and Automation Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781839539954

Airplane detection in remote sensing image has attracted increasing attention in recent years owing to the successful applications of civil and military. Usually, the structure of the airplane is symmetrical in order to ensure the flight safety, and this inherent character would benefit target detection from complex background. This paper proposes a novel airplane detection algorithm based on symmetry-based region proposal (SB-RP) method and convolutional neural network (CNN). Firstly, pre-trained convolutional neural network is used to extract the high-level features from the training data set that include positive samples (airplane) and negatives (background). Then a symmetry-based region proposal (SB-RP) method is developed to extract the regions of interest from the complex remote sensing image. At last, the convolutional neural network trained by high-level features is exploited to detect the airplane from these regions of interest. The major contribution of SB-RP CNN method is that the interference of background clutter is greatly decrease for the regions of interest are reduced significantly for the background isn't symmetrical. The extensive experiments are induced and the experimental results show that the SB-RP CNN method could greatly improve the performance of airplane detection in remote sensing image. © The Institution of Engineering & Technology 2023.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Investigating the influence of hyperspectral data compression on spectral unmixing 28

Investigating the influence of hyperspectral data compressio...

引用

conference on image and signal processing for remote sensing XXVIII

作者： Kuester, Jannick Anastasiadis, Johannes Middelmann, Wolfgang Heizmann, Michael Fraunhofer Ctr Machine Learning Fraunhofer IOSB Ettlingen Germany Karlsruhe Inst Technol KIT Inst Ind Informat Technol IIIT Hertzstr 16 D-76187 Karlsruhe Germany

ISBN: (纸本)9781510655386;9781510655379

This work addresses the problem of hyperspectral data compression and the evaluation of the reconstruction quality for different compression rates. Data compression is intended to transmit the enormous amount of data created by hyperspectral sensors efficiently. The information loss due to the compression process is evaluated by the complex task of spectral unmixing. We propose an improved 1D-Convolutional Autoencoder architecture with different compression rates for lossy hyperspectral data compression. Furthermore, we evaluate the reconstruction by applying metrics such as SNR and SA and compare them to the spectral unmixing results.

关键词： hyperspectral data compression spectral unmixing feature extraction dimensionality reduction Autoencoder hyperspectral imaging spectral analysis remote sensing convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Weakly supervised ship detection in remote sensing images 28

Weakly supervised ship detection in remote sensing images

引用

conference on image and signal processing for remote sensing XXVIII

作者： Guo, Chen Tan, Zhiwen An, Meng Jiang, Zhiguo Zhang, Haopeng Beihang Univ Dept Aerosp Informat Engn Sch Astronaut Beijing 102206 Peoples R China CAST Inst Spacecraft Syst Engn Beijing 100094 Peoples R China Beijing Key Lab Digital Media Beijing 102206 Peoples R China Minist Educ Key Lab Spacecraft Design Optimizat & Dynam Simu Beijing 102206 Peoples R China

ISBN: (纸本)9781510655386;9781510655379

Ship detection in remote sensing images is important for maritime surveillance. With the rapid development of earth observation technology, high-resolution imaging satellites can provide more observational information. In the face of massive remote sensing data, object-level annotation requires a lot of time and manpower. Weakly supervised object detection is trained using only image-level annotations, thus reducing the requirement for object-level annotations. However, there are still some problems in the detection of weakly supervised ships in remote sensing images, because of the complex, dense distribution and diverse scale characteristics of the ship environment. We propose a weakly supervised object detection method that combines Transformer with weakly supervised learning for ship detection in remote sensing images. First, Proposal Clustering Learning (PCL) for weakly supervised object detection is used as the baseline to detect ships, and the network is continuously refined for better detection performance. Second, the prior location and size information is added to the features of the proposal through the transformer module. These additional information can be used as an important basis for judging whether the proposal is optimal, thereby improving the detection performance. To evaluate the effectiveness of our method, extensive experiments are conducted on a complex dataset of large-scene remote sensing ships. Experimental results show that our method achieves better detection performance than other methods.

关键词： Weak supervision ship detection proposal clustering transformer remote sensing

来源：评论

学校读者我要写书评

暂无评论

remote sensing image Ship Detection Based on Improved YOLOv3 7

Remote Sensing Image Ship Detection Based on Improved YOLOv3

引用

7th International conference on Intelligent Computing and signal processing, ICSP 2022

作者： Da, Yifan Gao, Xing Li, Mengfei Beijing University of Chemical Technology School of Information and Communication Technology Beijing China Yanshan University Liren College Qinhuangdao China Beijing University of Posts and Telecommunications Computer Science and Technology Beijing China

ISBN: (纸本)9781665478571

With the improvement of remote sensing image resolution, remote sensing target detection has become a research hotspot, and it plays an important role in military reconnaissance, disaster rescue, urban traffic management, and other scenarios. However, remote sensing target detection still has problems such as a large amount of data and specific differences in shooting by different sensors. In order to meet these challenges and improve the speed and accuracy of remote sensing target detection, this paper proposes a target detection algorithm based on an attention mechanism. Based on the YOLOv3 network, the SE-YOLOv3 network is designed using an attention mechanism to increase expressiveness, focus on essential features, and suppress unnecessary features. The experimental results show that: on the DOTA data set, compared with the YOLOv3 network, adding the squeeze and excitation (SE) model to the bottleneck has the best effect and can better improve the performance of the model. Experiments show that the proposed SE-YOLOv3 network is more efficient and accurate than YOLOv3, so it is more suitable for target detection tasks in complex remote sensing scenes. © 2022 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Semi-supervised classification method for PolSAR Based on vision transformer (ViT) 4

Semi-supervised classification method for PolSAR Based on vi...

引用

4th International conference on signal image processing and Communication, ICSIPC 2024

作者： Li, Xiangshen Yu, Jie Han, Lihua College of Resource Environment and Tourism Capital Normal University Beijing100048 China Beijing Laboratory of Water Resources Security Capital Normal University Beijing100048 China State Key Laboratory Incubation Base of Urban Environmental Process and Digital Simulation Capital Normal University Beijing100048 China Land Satellite Remote Sensing Application Center of MNR Beijing100048 China

ISBN: (纸本)9781510682467

Semi-supervised classification methods generate pseudo-labels from unlabeled data, where pseudo-labels' precisio n is vital for successful classification outcomes. Addressing the challenge of inaccuracies in pseudo-label catego ries during training, this paper introduces a semi-supervised classification strategy for PolSAR images, leveragin g the Vision Transformer (ViT) technology. In this study, the Simple Linear Iterative Clustering (SLIC) method was applied to create superpixel blocks with distinct boundaries and organized structures, facilitating the generat ion of pseudo-labels with a correctness rate of 85.9%. This paper introduces the Vision Transformer (ViT) net work, capitalizing on its multi-head attention mechanism. This approach enhances global image information extr action while simultaneously addressing local details for pseudo-label training. Specifically, on the Flevoland data set, an increase in pseudo-label category accuracy of 7.09% and 5.47% was achieved. For the Wuhan Tongshu n River dataset, improvements of 2.44% and 3.41% in pseudo-label category accuracy were recorded, thereby e levating the precision of PolSAR semi-supervised classification. © 2024 SPIE.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

A NEW DEEP LEARNING METHOD FOR MULTISPECTRAL image TIME SERIES COMPLETION USING HYPERSPECTRAL DATA 47

A NEW DEEP LEARNING METHOD FOR MULTISPECTRAL IMAGE TIME SERI...

引用

47th IEEE International conference on Acoustics, Speech and signal processing (ICASSP)

作者： Cisse, C. T. Alboody, A. Puigt, M. Roussel, G. Vantrepotte, V. Jamet, C. Tran, T. K. Univ Littoral Cote dOpale LISIC UR 4491 F-62219 Longuenesse France Univ Littoral Cote dOpale CNRS LOG UMR 8187 F-62930 Wimereux France

ISBN: (纸本)9781665405409

The massive development of remote sensing allowed many novel applications which bring new challenges. In particular, some applications such as marine observation require a good spatial, spectral, and temporal resolution. In order to tackle the last issue, spatio-temporal fusion of remote sensing data allows to complete a time series of multispectral images from, e.g., hyperspectral images. In this paper, we propose a new deep learning approach to that end. Our main contribution lies in the error completion task which allows to improve the completion performance. We show that our proposed method is able to produce high fidelity predictions with better quality indices than state-of-the-art methods on true images taken from the CIA / LGC database and Sentinel-2 / Sentinel-3 data.

关键词： remote sensing Time-Series Completion Spatio-Temporal Fusion 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362页 << < 23 24 25 26 27 28 29 30 31 32 > >>

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：