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331-340 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：

Scale-Spectral-Spatial Attention Network for Hyperspectral image Classification 30

Scale-Spectral-Spatial Attention Network for Hyperspectral I...

引用

30th IEEE signal processing and Communications Applications conference (SIU)

作者： Derbashi, Usama Aptoula, Erchan Gebze Tech Univ Comp Engn Dept Kocaeli Turkey Gebze Tech Univ Inst Informat Technol Kocaeli Turkey

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450928

Attention networks enable neural networks to focus on the most beneficial parts of their input. In the context of remote sensing image classification, studies about spatial, spectral and spatial-spectral attention networks have already been reported. In this paper, a network integrating a scale-based attention module, in addition to spatial-spectral attention is proposed. The scale-space has been produced via alpha-trees, in order for the network to focus on the most useful scales. It is tested with two real hyperspectral datasets, where it achieves a performance improvement.

关键词： Attention network machine learning pixel classification hyperspectral images tree representation

来源：评论

学校读者我要写书评

暂无评论

SlotFusion: Object-Centric Audiovisual Feature Fusion with Slot Attention for remote sensing Scene Recognition

SlotFusion: Object-Centric Audiovisual Feature Fusion with S...

引用

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

作者： Fangzhou Han Tianyi Yu Lamei Zhang Lingyu Si Yiqi Zhang Dept. of Information Engineering Harbin Institute of Technology Harbin China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Despite significant advancements in remote sensing multimodal learning, particularly in image-image feature fusion, the exploration of audio-image feature fusion remains insufficient. Given the complexity and redundancy of ground objects in remote sensing images, accurately aligning audio features with image features during the fusion process is a critical challenge. In this paper, we introduce an object-centric feature fusion method named SlotFusion. By employing a slot attention-based feature decoupling module and a slot-based audiovisual feature fusion module, we transform modality features with complex semantic information into a set of slot features corresponding to object units and use gated activation units to adaptively implement object-centric feature fusion. Experiments on the Audio Visual Aerial Scene Recognition dataset (ADVANCE) demonstrate that the proposed SlotFusion significantly improves remote sensing scene recognition performance, with a 7.04% increase in overall accuracy compared to previous methods, achieving state-of-the-art results.

关键词： Visualization Semantics Termination of employment Transforms Interference signal processing Logic gates Robustness Speech processing remote sensing

来源：评论

学校读者我要写书评

暂无评论

Spatio-temporal monitoring of soil salinity and land cover changes using remote sensing techniques: Zaghouan case study (Tunisia) 6

Spatio-temporal monitoring of soil salinity and land cover c...

引用

6th International conference on Advanced Technologies for signal and image processing, ATSIP 2022

作者： Saad, Karem Kallel, Amjad Rebah, Zouhaier Ben Solaiman, Basel University of Sfax 3E Laboratory National Engineering School of Sfax Sfax Tunisia National Center for Mapping and Remote Sensing Tunis Tunisia Imt Atlantique Telecom Bretagne Brest France

ISBN: (纸本)9781665451161

Expansion of salt-induced soil in agriculture lands is major cause of desertification and crop yield loss. Detection of salt affected soils and land cover changes assessment is crucial to better understand the correlations between diverse land cover units and *** variations at local scales can be studied using a combination of satellite image analysis and field *** purpose of this study is to assess and detect the variations of salt content in the soil and land cover in the study area located in Zaghouan Governorate (Tunisia). This includes the examination of soil salinization effect on land cover change between 2017 and *** imagery has shown that the study area has different soil cover *** on the combination between satellite imagery analysis and soil sampling, findings reveal a slight correlation between salinity levels and land cover *** study of soil salinity changes shows that salt content rates increased from 5.44 % in 2017 to 6.7 % in 2021, while the land cover has reversely changed to 9.95 % for the same time ***, results related to the relationship between SI and NDVI indicates that soil salinity changes have a significant and direct impact on land cover over *** addition to in situ data, comparing Sentinel-2 and Landsat 8 satellite data shows a great potential for detecting and monitoring salt-affected soils © 2022 IEEE.

关键词： Soils

来源：评论

学校读者我要写书评

暂无评论

Research on hyperspectral image classification based on improved deep cross-domain few-shot learning 5

Research on hyperspectral image classification based on impr...

引用

5th International conference on Video, signal and image processing, VSIP 2023

作者： Luo, Xinhua Wang, Qingyan Liang, Xintao Zhang, Zhen School Of Measurement And Control Technology And Tele Communications Engineering Harbin University Of Science And Technology Harbin150080 China

ISBN: (纸本)9798400709272

In this study, an enhanced method and system for hyperspectral image classification are presented, based on deep cross-scene few-shot learning. This pertains to the domain of remote sensing image processing technology and addresses the prevalent issues of inadequate classification performance in existing techniques for hyperspectral image categorization. The core aspects of this invention encompass the following: utilization of two mapping layers to standardize the input dimensions between the source and target domains;the deployment of an embedded feature extractor to incorporate the image cubes from both the source and target domains into a space-spectral embedding environment simultaneously, ensuring that like samples are closely aligned and dissimilar ones are distanced. Through gauging the distances between each class of unlabeled and labeled samples in this space-spectral embedding zone, learning with a few number of examples in both the source and target domains is achieved. Furthermore, a conditional domain discriminator is employed to mitigate domain shifts between domains, thus solidifying the domain stability of the extracted spatial-spectral embedding features. This innovative approach allows for high-precision hyperspectral data categorization, even when only a few examples are available. © 2023 ACM.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

Evaluating the label efficiency of contrastive self-supervised learning for multi-resolution satellite imagery 28

Evaluating the label efficiency of contrastive self-supervis...

引用

conference on image and signal processing for remote sensing XXVIII

作者： Bourcier, Jules Dashyan, Gohar Chanussot, Jocelyn Alahari, Karteek Univ Grenoble Alpes CNRS Grenoble INP LJK F-38000 Grenoble France Preligens Ex Earthcube F-75000 Paris France

ISBN: (纸本)9781510655386;9781510655379

The application of deep neural networks to remote sensing imagery is often constrained by the lack of ground-truth annotations. Adressing this issue requires models that generalize efficiently from limited amounts of labeled data, allowing us to tackle a wider range of Earth observation tasks. Another challenge in this domain is developing algorithms that operate at variable spatial resolutions, e.g., for the problem of classifying land use at different scales. Recently, self-supervised learning has been applied in the remote sensing domain to exploit readily-available unlabeled data, and was shown to reduce or even close the gap with supervised learning. In this paper, we study self-supervised visual representation learning through the lens of label efficiency, for the task of land use classification on multi-resolution/multi-scale satellite images. We benchmark two contrastive self-supervised methods adapted from Momentum Contrast (MoCo) and provide evidence that these methods can be perform effectively given little downstream supervision, where randomly initialized networks fail to generalize. Moreover, they outperform out-of-domain pretraining alternatives. We use the large-scale fMoW dataset to pretrain and evaluate the networks, and validate our observations with transfer to the RESISC45 dataset.

关键词： deep learning computer vision remote sensing self-supervised learning land use classification label-efficient learning optical imagery

来源：评论

学校读者我要写书评

暂无评论

image Fusion with CT and MRI images for Improving the Quality of Diagnostics 8th

Image Fusion with CT and MRI Images for Improving the Qualit...

引用

8th International conference on Computers, Management and Mathematical Sciences, ICCM 2022

作者： Supriya, Bopparaju Keerthi, Jajula Om Sri Rachel, Peeka Ushasvi Manjari, Usha Shaik, Subhani Department of Information Technology Sreenidhi Institute of Science & Technology Telangana Hyderabad India

ISBN: (纸本)9783031511660

To create a single image with the most information possible, two photographs of same model are combined through the process of image fusion. Many image-processing applications, including satellite imaging, remote sensing, and picture fusion, are crucial. The study of fusing images has developed to support the development of the above applications, and it has since expanded to include research on medical imaging. The merger of magnetic resonance (MR) and computerized tomography (CT) images is accomplished by using a curvelet-based method, which is presented in this chapter. The goal of merging an MR image and a CT image of the same organ is to create a unique image that contains the most diagnostically useful information on that organ. Wavelet transform has been suggested in some attempts to fuse MR and CT images. Given the number of items and curved forms in medical photos and after fusion, the curvelet transform is expected to perform better than the wavelet transform. According to the simulation results, the curvelet transform outperforms the wavelet transform when merging MR and CT images, in terms of visual quality and peak signal-to-noise ratio (PSNR). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image fusion

来源：评论

学校读者我要写书评

暂无评论

MASKFORMER WITH IMPROVED ENCODER-DECODER MODULE FOR SEMANTIC SEGMENTATION OF FINE-RESOLUTION remote sensing imageS 29

MASKFORMER WITH IMPROVED ENCODER-DECODER MODULE FOR SEMANTIC...

引用

IEEE International conference on image processing (ICIP)

作者： Li, Zhuoxuan Yang, Junli Wang, Bin Li, Yaqi Pan, Ting Beijing Univ Posts & Telecommun Beijing Peoples R China China Mobile Res Inst Beijing Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

In 2021, the Transformer based models have demonstrated extraordinary achievement in the field of computer vision. Among which, Maskformer, a Transformer based model adopting the mask classification method, is an outstanding model in both semantic segmentation and instance segmentation. Considering the specific characteristics of semantic segmentation of remote sensing images (RSIs), we design CADA-MaskFormer(a Mask classification-based model with Cross-shaped window self-Attention and Densely connected feature Aggregation) based on Maskformer by improving its encoder and pixel decoder. Concretely, the mask classification that generates one or even more masks for specific category to perform the elaborate segmentation is especially suitable for handling the characteristic of large within-class and small between-class variance of RSIs. Furthermore, we apply the Cross-Shaped Window self-attention mechanism to model the long-range context information contained in RSIs at maximum extent without the increasing of computational complexity. In addition, the Densely Connected Feature Aggregation Module (DCFAM) is used as the pixel decoder to incorporate multi-level feature maps from the encoder to get a finer semantic segmentation map. Extensive experiments conducted on two remotely sensed semantic segmentation datasets Potsdam and Vaihingen achieves 91.88% and 91.01% in OA index respectively, outperforming most of competitive models designed for RSIs. The code is available from https://***/lqwrl542293/JL-Yang_CV/tree/master/CADA_Maskformer

关键词： Semantic segmentation remote sensing images Transformer

来源：评论

学校读者我要写书评

暂无评论

LE-BEIT: A LOCAL-ENHANCED SELF-SUPERVISED TRANSFORMER FOR SEMANTIC SEGMENTATION OF HIGH RESOLUTION remote sensing imageS 29

LE-BEIT: A LOCAL-ENHANCED SELF-SUPERVISED TRANSFORMER FOR SE...

引用

IEEE International conference on image processing (ICIP)

作者： Huang, Yifei Feng, Zideng Yang, Junli Wang, Bin Wang, Jiaying Xian, Zhenglin Beijing Univ Posts & Telecommun Beijing Peoples R China China Mobile Res Inst Beijing Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Semantic segmentation for remote sensing images (RSI) has been a thriving research topic for a long time. Existing supervised learning methods usually require a huge amount of labeled data. Meanwhile, large size, variation in object scales, and intricate details in RSI make it essential to capture both long-range context and local information. To address these problems, we propose Le-BEIT, a self-supervised Transformer with an improved positional encoding Local-Enhanced Positional Encoding (LePE). Self-supervised learning relieves the demanding requirement of a large amount of labeled data. The self-attention mechanism in Transformer has remarkable capability in capturing long-range context. Meanwhile, we use LePE as a substitution for Relative Positional Encoding (RPE) to represent local information more effectively. Moreover, considering the domain difference between natural images and RSI, instead of imageNet-22K, we pre-train Le-BEIT on a very small high-resolution RSI dataset-GID. To investigate the influence of pre-training dataset size on segmentation accuracy, we furtherly conduct experiments on a larger pre-training dataset called GID-DOTA, which is 1/100 of imageNet-22K, and have observed considerable accuracy improvements. The result of our method, which relies on a much smaller pretrained dataset, achieves competitive accuracy compared to the counterpart on imageNet-22K.

关键词： remote sensing Self-supervised Learning Transformer

来源：评论

学校读者我要写书评

暂无评论

A 2-D Re-Focusing Algorithm for Spotlight UAV SAR imagery 3

A 2-D Re-Focusing Algorithm for Spotlight UAV SAR Imagery

引用

3rd International conference on signal image processing and Communication, ICSIPC 2023

作者： Xu, Weidi Yang, Xiaoliang You, Pengjie Wang, Lin Xia, Yewei Feng, Shuo The 54th Research Institute of CETC No.589 Zhongshan West Road Qiaoxi District Hebei Shijiazhuang China Hebei Key Laboratory of Intelligent Information Perception and Processing Shijiazhuang050081 China

ISBN: (纸本)9781510670945

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is an efficient remote sensing system. It has been used widely in both the military field and civilian field. However, when it works in spotlight mode, the accumulated motion errors can be heavy over the long synthetic time, inevitably causing two-dimensional (2-D) defocus. In this paper, a novel two-step autofocus algorithm based on the coarse imaging result is developed. The estimation of azimuth as well as range error is operated in the range-compress azimuth-frequency domain and the compensation is completed with the 2-D frequency spectrum correction. Simulation experiments verified the effectiveness of the proposed method. © 2023 SPIE.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

image processing and Control of Tracking Intelligent Vehicle Based on Grayscale Camera 5

Image Processing and Control of Tracking Intelligent Vehicle...

引用

5th IEEE International conference on image processing Applications and Systems (IPAS)

作者： Zhang, Jian Liu, Yufan Li, Ao Zeng, Jinshan Xie, Hongtu Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen Campus Shenzhen Peoples R China Nanchang Hangkong Univ Sch Informat Engn Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781665462198

In order to realize the rapid and stable recognition and automatic tracking of various complex roads by the intelligent vehicles, this paper proposes image processing and cascade Proportion Integration Differentiation (PID) steering and speed control algorithms based on CMOS grayscale cameras in the context of the national college student intelligent vehicle competition. First, the grayscale image of the track is acquired by the grayscale camera. Then, the Otsu method is used to binarize the image, and the information of black boundary guide line is extracted. In order to improve the speed of the race, various track elements in the image are identified and classified, and the deviation between the actual centerline position and the ideal centerline position of the intelligent vehicle is calculated. Third, the discrete incremental cascade PID control algorithm is used to calculate the pulse width modulation (PWM) signal corresponding to the deviation. And the PWM signal is acted on the steering motor through the driving circuit, driving the intelligent vehicle to always drive along the middle road, so as to achieve the purpose of automatic tracking guidance. Experiments prove that the intelligent vehicle of this design can identify complex roads quickly and in a stable way, accurately complete automatic tracking, and obtain higher speed performance.

关键词： tracking intelligent vehicle proportional integral derivative control CMOS grayscale camera binary image image processing

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 << < 30 31 32 33 34 35 36 37 38 39 > >>

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：