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

3,540 篇 会议
35 篇 期刊文献
8 册 图书

馆藏范围

3,583 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,553 篇 工学
- 1,041 篇 电气工程
- 993 篇 计算机科学与技术...
- 892 篇 测绘科学与技术
- 712 篇 信息与通信工程
- 526 篇 软件工程
- 221 篇 光学工程
- 188 篇 仪器科学与技术
- 157 篇 控制科学与工程
- 155 篇 电子科学与技术（可...
- 154 篇 航空宇航科学与技...
- 90 篇 生物工程
- 83 篇 机械工程
- 64 篇 化学工程与技术
- 61 篇 生物医学工程（可授...
- 51 篇 建筑学
- 47 篇 土木工程
- 43 篇 环境科学与工程（可...
1,427 篇 理学
- 1,017 篇 物理学
- 246 篇 数学
- 102 篇 生物学
- 93 篇 系统科学
- 84 篇 化学
- 84 篇 统计学（可授理学、...
- 42 篇 大气科学
- 29 篇 地球物理学
- 29 篇 地质学
677 篇 医学
- 675 篇 临床医学
171 篇 管理学
- 96 篇 图书情报与档案管...
- 80 篇 管理科学与工程(可...
38 篇 农学
- 30 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

1,082 篇 remote sensing
202 篇 image processing
174 篇 feature extracti...
168 篇 image segmentati...
167 篇 synthetic apertu...
155 篇 satellites
137 篇 signal processin...
118 篇 image resolution
111 篇 image reconstruc...
110 篇 hyperspectral im...
105 篇 image classifica...
104 篇 image fusion
101 篇 deep learning
95 篇 image analysis
90 篇 spatial resoluti...
89 篇 radar imaging
87 篇 signal processin...
87 篇 sensors
86 篇 image coding
70 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...
9 篇 beijing inst spa...
9 篇 school of artifi...
9 篇 state key labora...

作者

36 篇 bruzzone lorenzo
31 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 zhang haopeng
11 篇 benediktsson jon...
10 篇 middelmann wolfg...
10 篇 yuan yuan
9 篇 abramov sergey k...
9 篇 li yao
9 篇 datcu mihai

语言

3,490 篇 英文
52 篇 其他
32 篇 土耳其文
8 篇 中文
2 篇 俄文

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

共 3583 条记录，以下是521-530 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：

Hierarchical Probabilistic Graphical Models and Deep Convolutional Neural Networks for remote sensing image Classification 29

Hierarchical Probabilistic Graphical Models and Deep Convolu...

引用

29th European signal processing conference (EUSIPCO)

作者： Pastorino, Martina Moser, Gabriele Serpico, Sebastiano B. Zerubia, Josiane Univ Genoa DITEN Dept Genoa Italy Univ Cote dAzur INRIA Sophia Antipolis France

ISBN: (纸本)9789082797060

The method presented in this paper for semantic segmentation of multiresolution remote sensing images involves convolutional neural networks (CNNs), in particular fully convolutional networks (FCNs), and hierarchical probabilistic graphical models (PGMs). These approaches are combined to overcome the limitations in classification accuracy of CNNs for small or non-exhaustive ground truth (GT) datasets. Hierarchical PGMs, e.g., hierarchical Markov random fields (MRFs), are structured output learning models that exploit information contained at different image scales. This perfectly matches the intrinsically multiscale behavior of the processes of a CNN (e.g., pooling layers). The framework consists of a hierarchical MRF on a quadtree and a planar Markov model on each layer, modeling the interactions among pixels and accounting for both the multiscale and the spatial-contextual information. The marginal posterior mode criterion is used for inference. The adopted FCN is the U-Net and the experimental validation is conducted on the ISPRS 2D Semantic Labeling Challenge Vaihingen dataset, with some modifications to approach the case of scarce GTs and to assess the classification accuracy of the proposed technique. The proposed framework attains a higher recall compared to the considered FCNs, progressively more relevant as the training set is further from the ideal case of exhaustive GTs.

关键词： remote sensing semantic segmentation multiresolution hierarchical PGM CNN

来源：评论

学校读者我要写书评

暂无评论

Weak Energy Interference Suppression in InSAR image Using Semantic Segmentation Network

Weak Energy Interference Suppression in InSAR Image Using Se...

引用

IET International Radar conference 2023, IRC 2023

作者： Li, Jiawang Gong, Yanyun Tang, Chuheng Tao, Mingliang Su, Jia Northwestern Polytechnical University Xi'an710072 China Shanghai Institute of Satellite Engineering Shanghai201109 China

ISBN: (纸本)9781839539954

Spaceborne interferometric synthetic aperture radar (InSAR) satellite, serves multiple industries, including land management, earthquake analysis, mapping, environmental monitoring, disaster reduction, and forestry. However, the presence of radio frequency interference (RFI) makes it difficult to obtain high-quality digital elevation model (DEM) and to perform deformation monitoring. In this paper, a RFI suppression model based on Residual Attention UNet (RA-UNet) is proposed for the Lutan-1 ground processing system, which is capable of processing massive images quickly and efficiently. The model combines attention mechanism and residual block to quickly focus on the changes in the local interference region, and uses UNet to extract the interference features in the spectrum to distinguish the interference. Under different RFI conditions, a wide range of experimental results are provided in different scenarios of the real measured data of Lutan-1, including single-pass and repeat-pass modes. The experimental results demonstrate the effectiveness, efficiency and robustness of the proposed method in interference suppression. © The Institution of Engineering & Technology 2023.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Stripe Noise Removal from remote sensing images 6

Stripe Noise Removal from Remote Sensing Images

引用

6th IEEE International conference on signal processing, Computing and Control, ISPCC 2021

作者： Satya, P.Mohana Jagadish, Samudrala Satyanarayana, V. Singh, Mahesh K Aditya Engineering College Department Of ECE Surampalem India New Delhi India

ISBN: (纸本)9781665425520

remote sensing images are in many domains used, including geographic, military, urban planning and environmental surveillance, but they are somewhat limiting their application due of additional stripe noise. Clear images from stripe pictures may be easily predicted in most existing stream noise reduction algorithms without considering the underlying characteristics of strip noise that cause the structure to be destroyed. Thus a new strategy was suggested in this study from the point of view of the image breakdown. The inherent qualities of strip noise and image properties are taken into consideration. The suggested methodology combines regularization, group regulation and television regularization in a framework for picture decomposition, into a (TV). The first two terms are used to execute stripe noise qualities through statistical analyses and regularization of the TV should evaluate the portions of the smooth structures of the stripe-free image. In addition, an effective alternating minimization methodology is proposed to solve the picture decomposition model. © 2021 IEEE.

关键词： remote sensing

来源：评论

学校读者我要写书评

暂无评论

remote sensing Satellite image Analysis for Deforestation in Yavatmal District, Maharashtra, India 3

Remote Sensing Satellite Image Analysis for Deforestation in...

引用

3rd International conference on signal processing and Communication (ICPSC)

作者： Nanare, Indersheel Kaur H. S. Bhoyar, Dinesh B. Balamwar, Sanjay V. Yeshwantrao Chavan Coll Engn Dept Elect & Telecommun Engn Nagpur Maharashtra India Applicat Ctr Maharashtra Remote Sensing Nagpur Maharashtra India

ISBN: (纸本)9781665428644

Forests are the most important and essential natural resources used by mankind for various usage. Loss of these forests has emerged as a major environmental concern in recent years. But for the purpose of development, many forests are being cut resulting in deforestation. Due to this cutting of forests, deforestation rate is increasing day by day and the world is facing severe disasters. Therefore, this continuous clearing of forests has a major degraded impact on the soil quality, climate change, hydrological cycle and ecosystem etc. This paper suggests suitable remote sensing techniques which can be used for the analyses of planet scope 3m resolution satellite data of Yavatmal district of Maharashtra state. remote sensing techniques along with the GIS i.e. geographic information system are used to detect the changes in forest cover of Yavatmal district. For recognition of forest cover, layer stacking of bands 3 and 4 by using Erdas Imagine software is done. For forest classification, the normalized difference vegetation index (NDVI) is experimented. The NDVI values reveal the change of forest cover and various other categories resulting in increase and decrease of categories. It is concluded that out of total forest area (76740.32 hectare), decreased category shows 29.44% due to forest cutting, forest fires and leaf shedding etc.

关键词： remote sensing deforestation forest cover enhancement thresholding Normalized difference vegetation index(NDVI)

来源：评论

学校读者我要写书评

暂无评论

An Unsupervised Domain Adversarial Wavelet Learning Method for registration of Optical and SAR Heterogeneous remote sensing images

An Unsupervised Domain Adversarial Wavelet Learning Method f...

引用

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

作者： Hongze Ye Shenfu Zhang Liangzhi Li Xiangchao Meng School of Information Science and Engineering Ningbo University Ningbo China College of Geological Engineering and Geomatics Chang’an University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Optical images and synthetic aperture radar (SAR) remote sensing images are highly complementary information for Earth observation and applications. Accurate registration of these two data types is essential for subsequent collaboration processing and analysis. However, traditional methods struggle to achieve effective registration due to nonlinear radiometric disparities and geometric distortions. Meanwhile, supervised deep learning approaches require costly manual annotations. To address these issues, this paper proposes an unsupervised registration method based on domain adversarial wavelet learning. We innovatively design an inter-domain adversarial mutual transfer (IAT) module to alleviate cross-modal differences while preserving structural features. Additionally, wavelet transform is employed at different scales for feature matching, to mitigate radiometric disparities and enhance feature recognizability. By incorporating an attention mechanism, the network structure is optimized through spatial alignment and image similarity. The proposed method outperforms other state-of-the-art methods in both quantitative and qualitative aspects of benchmark datasets.

关键词： Optical filters Wavelet transforms Wavelet domain Optical distortion Radiometry Optical imaging Optical sensors Nonlinear optics Synthetic aperture radar remote sensing

来源：评论

学校读者我要写书评

暂无评论

Physically Plausible Spectral Reconstruction in remote sensing Using Multispectral image

Physically Plausible Spectral Reconstruction in Remote Sensi...

引用

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

作者： Wenbin Chen Xiyang Zhi Yuanxin Huang Zhipeng Wang Zheng Lu Wei Zhang Research Center for Space Optical Engineering Harbin Institute of Technology Harbin China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Hyperspectral imaging technology offers significant advantages in remote sensing, capturing richer and more detailed spectral information in narrower bands compared to multispectral images. This makes it highly effective for land cover classification, crop monitoring, and environmental monitoring. However, the high cost, low spatial resolution, and complexity of data acquisition limit its widespread application. This study proposes a novel and flexible framework for spectral reconstruction designed to overcome these challenges. Our framework incorporates radiometric calibration, atmospheric correction, classification, spectral unmixing, and thermal infrared super-resolution, all of which can be replaced with the latest high-performance algorithms. Additionally, the physically plausible framework considers actual optical transmission processes, ensuring accurate representation of both reflective and radiative properties. This adaptable approach facilitates cost-effective and scalable acquisition of hyperspectral data. Experimental results demonstrate the accuracy and effectiveness of the proposed method, with reconstructed images showing high consistency with measured data, both in spectral curve shapes and radiance levels, underscoring its significant application potential.

关键词： Accuracy Superresolution Land surface Radiometry Optical imaging Optical sensors Optical reflection Spatial resolution image reconstruction Hyperspectral imaging

来源：评论

学校读者我要写书评

暂无评论

The Discrete System of Fibonacci Functions for the Construction of a Global Automated System for Monitoring Objects Littering the Environment Using remote sensing 27

The Discrete System of Fibonacci Functions for the Construct...

引用

conference on image and signal processing for remote sensing XXVII

作者： Kazaryan, M. Semenishchev, E. Voronin, V. North Ossetian State Med Acad Vladikavkaz Russia Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

The article explores the detection of unauthorized landfills based on remote sensing data. We have developed a mathematical approach that translates ordinary images to the 2-D manifold or 3-D manifolds in stereo images. At the same time, the posed task when developing algorithms is the transformation (convolution) of these manifolds into a one-dimensional sample. The developed method corresponds to the following two conditions: 1) preservation of the topological proximity of the elements of the original and expanded spaces, 2) preservation of correlations between the elements of the original and transformed spaces. The basic idea that underlies the mathematical support of the developed automated system is the concept of fractal sets. The concept of continuous orthogonal transformations, including the Fibonacci transform, is used as a mathematical basis for determining anomalous signal structures in the surrounding background. The problem of monitoring and decoding space images from the point of view of the synthesis of orthogonal systems with predetermined properties (speed of calculations, order of transformation, etc.) is presented. Examples of processing by the proposed algorithm are presented on the satellite images of the Moscow Region territories of the Russian Federation.

关键词： space image of waste disposal facility landfill chemical process waste degradation fractals discrete orthogonal transformations Fibonacci transformations

来源：评论

学校读者我要写书评

暂无评论

Local and Global Feature Adaptive Adjustment Network for remote sensing image Scene Classification

Local and Global Feature Adaptive Adjustment Network for Rem...

引用

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

作者： Feng Cao Chang Liu Deyu Li Yuhua Qian Chao Zhang Hu Zhang School of Computer and Information Technology (School of Big Data) Shanxi University Taiyuan China Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Shanxi University Taiyuan China Institute of Big Data and Industry Shanxi University Taiyuan China

Convolutional neural network (CNN)-based methods have been extensively used for remote sensing scene classification (RSSC) and have obtained remarkable classification results. However, its limitations in extracting global features have hindered further improvement. Transformers can directly capture global features through self-attention mechanisms, but they have deficiencies in modeling local features. Currently, an approach that directly combines CNN and Transformer features may lead to feature imbalance, and introduce redundant information. To address these problems, we propose a local and global feature adaptive adjustment network (LGFAANet) for RSSC. First, we employ a dual-branch network structure to extract local and global features from remote sensing scene images. Second, we design a local and global feature adaptive adjustment module (LGFAA) to dynamically allocate weights to the features. Third, we use a multi-layer feature aggregation module (MLFA) to aggregate the adjusted features, thereby further enhancing feature representation. Finally, we introduce joint loss to accelerate network convergence, while reducing intra-class distance and increasing inter-class distance. Experimental results demonstrate that our proposed method displays enhanced feature representation ability and outperforms existing state-of-the-art methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Change Feature Refinement and Cross-Scale Interaction Network for High-Resolution remote sensing image Change Detection

Change Feature Refinement and Cross-Scale Interaction Networ...

引用

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

作者： Yongqi Chen Shou Feng Yingjie Tang Nan Su Chunhui Zhao College of Information and Comminication Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Change detection represents a fundamental research area within remote sensing technology. Nevertheless, in practical applications, the spatial arrangement of terrestrial features is frequently highly complex. The changing objects vary significantly in size and exhibit irregular, complex shapes. Many mainstream methods are hindered by the intricate spatial scales of these changed objects, culminating in suboptimal detection performance. To overcome these obstacles, we propose a change feature refinement and cross-scale interaction network (FRCI-Net) for effective change detection. To capture more robust change features, FRCI-Net first employs a feature interaction module (FIM) and a differential feature refinement module (DFRM) to accurately capture change regions while enhancing the boundary details of the change features. To further enhance the recognition ability for multi-scale change targets, FRCI-Net leverages a cross-scale interaction module (CIM) to enable attention-driven integration and consolidation of change features across divers scales, thereby improving the network’s scale awareness of various change targets. Experiments conducted on two benchmark datasets validate that the proposed method surpasses six advanced methods.

关键词： Target recognition Shape Aggregates Benchmark testing Feature extraction Data processing remote sensing

来源：评论

学校读者我要写书评

暂无评论

Hazy remote sensing images Semantic Segmentation for Weakly Annotation Based on Saliency-Aware Alignment Strategy

Hazy Remote Sensing Images Semantic Segmentation for Weakly ...

引用

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

作者： Junda Xu Libao Zhang School of Artificial Intelligence Beijing Normal University Beijing China

The technique of semantic segmentation (SS) holds significant importance in the domain of remote sensing image (RSI) processing. The current research primarily encompasses two problems: 1) RSIs are easily affected by clouds and haze; 2) SS based on strong annotation requires vast human and time costs. In this paper, we propose a weakly supervised semantic segmentation (WSSS) method for hazy RSIs based on saliency-aware alignment strategy. Firstly, we design alignment network (AN) and target network (TN) with the same structure. After training the AN with clear images, we extract the class activation maps of the two networks and construct a consistency loss to train the TN with hazy images. Secondly, we design a multi-scale channel-spatial attention module in the two classification networks to solve the problems of unclear foreground-background boundary and blurred target texture in hazy images. Finally, the pseudo-labels generated by the TN are utilized to train a feedback saliency analysis network, which is subsequently employed for obtaining segmentation results during the testing phase. The experiment results demonstrate that our approach achieves superior SS performance for hazy RSIs.

关键词：

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359页 << < 49 50 51 52 53 54 55 56 57 58 > >>

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：