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
37 篇 期刊文献
8 册 图书

馆藏范围

3,615 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,582 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
- 711 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
52 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 3615 条记录，以下是441-450 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：

AFDN: ATTENTION-BASED FEEDBACK DEHAZING NETWORK FOR UAV remote sensing image HAZE REMOVAL

AFDN: ATTENTION-BASED FEEDBACK DEHAZING NETWORK FOR UAV REMO...

引用

IEEE International conference on image processing (ICIP)

作者： Wang, Shan Wu, Hanlin Zhang, Libao Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (纸本)9781665441155

To efficiently remove haze in unmanned aerial vehicle (UAV) remote sensing images, a novel attention-based feedback dehazing network (AFDN) is proposed, which is constructed by feedback connections and attention-based feedback blocks (AFBs). It has three major advantages compared with other dehazing algorithms: 1) The feedback connections, which allow network to use previous state to improve current performance, can effectively help the proposed AFDN generate clear remote sensing scenes progressively. 2) The AFBs are specially designed to extract global residual features, in which the dual attention block can usefully reduce redundant information and improve the fitting ability of network. 3) To obtain abundant texture information from UAV remote sensing images and restore real ground surfaces, an energy loss is employed for texture features learning. Experiments on synthetic datasets and real UAV remote sensing images verify the superiority of AFDN over several state-of-the-art methods in terms of qualitative and quantitative analysis.

关键词： image enhancement remote sensing image dehazing feedback mechanism CNN

来源：评论

学校读者我要写书评

暂无评论

Few-Shot Optical remote sensing Object Detection Based on Shape Matching

Few-Shot Optical Remote Sensing Object Detection Based on Sh...

引用

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

作者： Zhihui Wu Hong-Fang Yuan Fei Ma Fan Zhang College of Information Science and Technology Beijing University of Chemical Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Traditional object detection methods suffer from excessively high false alarm rates in scenarios with scarce training samples. To address this issue, this paper proposes a few-shot optical remote sensing object detection method based on shape matching. This method first obtains the sliced mask image of the object within the predicted bounding box in the pre-detection process. Then, the shape context algorithm is utilized to perform template matching between the template image and the sliced mask image, thereby eliminating false alarm objects and enhancing the object detection performance. To examine the capability of the proposed method, two public datasets, namely FAIR1M datasets and MAR20 datasets, are selected for experimentation. The experimental results show that our method achieves exceptional effectiveness, yielding F1 scores of 0.705 and 0.866 on the FAIR1M and MAR20 datasets, respectively.

关键词： Integrated optics YOLO Training Shape Optical imaging Prediction algorithms Data processing Optical sensors remote sensing

来源：评论

学校读者我要写书评

暂无评论

remote sensing image Classification Based on Densely Connected Convolutional Networks

Remote Sensing Image Classification Based on Densely Connect...

引用

2021 IEEE International conference on signal processing, Communications and Computing, ICSPCC 2021

作者： Gao, Ying Wu, Ming NorthwesternPolytechnical University School of Marine Science and Technology Xi'an China

ISBN: (纸本)9781665429184

With the emergence and advances of the concept 'Digital Earth', more applications require to use remote sensing images for analyzing or modeling observed scenes. remote sensing image classification thus becomes a very important part of the scene analysis. In recent ten years, deep neural networks have shown great advantages in the field of remote sensing image classification compared with other traditional methods. However, deep networks tend to encounter the problem of vanishing gradient. In this paper, we propose to use the Dense Convolutional Network (DenseNet) to conduct the remote sensing image classification task. DenseNet connects all layers directly to each other, that is, each layer in the network is connected to previous layers to reuse the features. Experiments with a real remote sensing image dataset validate the proposed method. © 2021 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Ship Target Detection in remote sensing image Based on Improved RetinaNet 4th

引用

4th International conference on 3D Imaging Technologies- Multidimensional signal processing and Deep Learning, 3D IT-MSP and DL 2022

作者： Sun, Yandong Fan, Tongliang Department of Maritime Command China Coast Guard Academy Ningbo315801 China Government Office Administration Bureau Beilun District Ningbo315801 China

ISBN: (纸本)9789819912292

Ship image target detection has important applications for ship management. In recent years, target detection based on deep learning has been widely studied in visual ship target detection. However, due to the difference and overlap of ship targets, the target loss rate is high. In order to solve the problem, this paper proposes a target detection algorithm based on improved RetinaNet for ship image target detection. The cyclical focal loss function and CIOU loss function are used to increase the training times of negative samples in the middle of training, which effectively increases ship detection precision by 2.5%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ships

来源：评论

学校读者我要写书评

暂无评论

Encoder-Minimal and Decoder-Minimal Framework for remote sensing image Dehazing

Encoder-Minimal and Decoder-Minimal Framework for Remote Sen...

引用

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

作者： Yuanbo Wen Tao Gao Ziqi Li Jing Zhang Ting Chen School of Information Engineering Chang’an University Xi’an China College of Engineering and Computer Science Australian National University Canberra ACT Australia

Haze obscures remote sensing images, hindering valuable information extraction. To this end, we propose RSHazeNet, an encoder-minimal and decoder-minimal framework for efficient remote sensing image dehazing. Specifically, regarding the process of merging features within the same level, we develop an innovative module called intra-level transposed fusion module (ITFM). This module employs adaptive transposed self-attention to capture comprehensive context-aware information, facilitating the robust context-aware feature fusion. Meanwhile, we present a cross-level multi-view interaction module (CMIM) to enable effective interactions between features from various levels, mitigating the loss of information due to the repeated sampling operations. In addition, we propose a multi-view progressive extraction block (MPEB) that partitions the features into four distinct components and employs convolution with varying kernel sizes, groups, and dilation factors to facilitate view-progressive feature learning. Extensive experiments demonstrate the superiority of our proposed RSHazeNet. We release the source code and all pre-trained models at https://***/chdwyb/RSHazeNet.

关键词：

来源：评论

学校读者我要写书评

暂无评论

AN AUTOFOCUS ALGORITHM FOR MOVING TARGET WITH MICRO-MOTION IN HIGH-RESOLUTION SAR image

AN AUTOFOCUS ALGORITHM FOR MOVING TARGET WITH MICRO-MOTION I...

引用

IET International Radar conference 2023, IRC 2023

作者： Jia, Boyu Dong, Xiao Zhang, Yunhua CAS Key Laboratory of Microwave Remote Sensing National Space Science Center Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781839539954

SAR (synthetic aperture radar) uses platform motion to observe stationary objects and synthesizes a larger antenna aperture to improve resolution, among which spotlight SAR further improves resolution through a longer synthetic aperture time. However, in this process, defocusing will occur in imaging moving objects, such as cars, ships, and airplanes. At the same time, if part of the moving object is in a different motion state from the subject, the image quality will deteriorate further. Therefore, this paper proposes a multi-PGA(phase gradient autofocus) algorithm for focusing moving target with micro-motion in high-resolution SAR image. Firstly, the PGA algorithm is applied to the defocused image to compensate the phase error caused by the movement of the main bodys of the target. Secondly, the target is divided into regions to focus the micro-motion of the wings. The overlapping parts of different regions are aligned to ensure the stability of the results. Finally, the open SAR data of the Umbra satellite of an moving airplane with vibrating wings is used to test the effectiveness of the proposed algorithm. © The Institution of Engineering & Technology 2023.

关键词： Radar imaging

来源：评论

学校读者我要写书评

暂无评论

TMANet: Triple Multi-Scale Attention based Network with Boundary Association Loss for Superpixel Segmentation

TMANet: Triple Multi-Scale Attention based Network with Boun...

引用

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

作者： Ziyi Zhang Shijie Lian Hua Li Hainan University Haikou China Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Superpixel segmentation with deep learning has been proposed in recent years and is widely employed to reduce the input image primitives for subsequent computer vision tasks. In this paper, we propose a Triple Multi-Scale Attention based Network (TMANet) for superpixel segmentation. First, aiming to extract more detailed context information, we design a Triple Multi-Scale Attention (TMA) to adapt the varying object scale and reduce inevitable redundant information in the encoder. Moreover, we observe that the dark parts with low probability in the association map generated by the TMANet are closer to the superpixel boundary. Therefore, we devise Boundary Association (BA) loss based on the association map to obtain fine boundaries and contours. Extensive experiments on public datasets show that TMANet outperforms the state-of-the-art methods to a certain extent. The application in saliency object detection of remote sensing also demonstrates the superiority of the proposed method.

关键词： Deep learning image segmentation Computer vision Object detection signal processing Acoustics Data mining Speech processing remote sensing

来源：评论

学校读者我要写书评

暂无评论

Locality Preserving Multiview Graph Hashing For Large Scale remote sensing image Search

Locality Preserving Multiview Graph Hashing For Large Scale ...

引用

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

作者： Wenyun Li Guo Zhong Xingyu Lu Chi-Man Pun Department of Computer and Information Science University of Macau Macau China

Hashing is very popular for remote sensing image search. This article proposes a multiview hashing with learnable parameters to retrieve the queried images for a large-scale remote sensing dataset. Existing methods always neglect that real-world remote sensing data lies on a low- dimensional manifold embedded in high-dimensional ambient space. Unlike previous methods, this article proposes to learn the consensus compact codes in a view-specific low-dimensional subspace. Furthermore, we have added a hyperparameter learnable module to avoid complex parameter tuning. In order to prove the effectiveness of our method, we carried out experiments on three widely used remote sensing data sets and compared them with seven state-of-the-art methods. Extensive experiments show that the proposed method can achieve competitive results compared to the other method.

关键词： Codes Quantization (signal) Feature extraction Sensors Noise measurement Task analysis Speech processing

来源：评论

学校读者我要写书评

暂无评论

UV-Mamba: A DCN-Enhanced State Space Model for Urban Village Boundary Identification in High-Resolution remote sensing images

UV-Mamba: A DCN-Enhanced State Space Model for Urban Village...

引用

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

作者： Lulin Li Ben Chen Xuechao Zou Junliang Xing Pin Tao School of Computer Technology and Applications Qinghai University Xining China School of Computer Science and Technology Beijing Jiaotong University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to the diverse geographical environments, intricate landscapes, and high-density settlements, the automatic identification of urban village boundaries using remote sensing images remains a highly challenging task. This paper proposes a novel and efficient neural network model called UV-Mamba for accurate boundary detection in high-resolution remote sensing images. UV-Mamba mitigates the memory loss problem in lengthy sequence modeling, which arises in state space models (SSM) with increasing image size, by incorporating deformable convolutions (DCN). Its architecture utilizes an encoder-decoder framework and includes an encoder with four deformable state space augmentation (DSSA) blocks for efficient multi-level semantic extraction and a decoder to integrate the extracted semantic information. We conducted experiments on two large datasets showing that UV-Mamba achieves state-of-the-art performance. Specifically, our model achieves 73.3% and 78.1% IoU on the Beijing and Xi’an datasets, respectively, representing improvements of 1.2% and 3.4% IoU over the previous best model while also being 6× faster in inference speed and 40× smaller in parameter count. Source code and pre-trained models are available at https://***/Devin-Egber/UV-Mamba.

关键词： Deformable models Location awareness image segmentation Source coding Semantics Neural networks Data mining Sustainable development Speech processing remote sensing

来源：评论

学校读者我要写书评

暂无评论

SKIP ATTENTION GAN FOR remote sensing image SYNTHESIS

SKIP ATTENTION GAN FOR REMOTE SENSING IMAGE SYNTHESIS

引用

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

作者： Deng, Kai Zhang, Kun Yao, Ping Cheng, Siyuan He, Peng Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9781728176055

High-quality remote sensing images are difficult to obtain due to limited conditions and high cost for data acquisition. With the development of machine vision and deep learning, some image generation methods (e.g., GANs) are introduced into this field, but it's still hard to generate images with good texture details and structural dependencies. We establish Skip Attention Mechanism to deal with this problem, which learns dependencies between local points on low-resolution feature maps, and then upsample the attention map and combine it with high-resolution feature maps. With this method, long-range dependencies learned from low-resolution are used for generating remote sensing images with more structural details. We name this method as Skip Attention GAN, which is the first method applying cross-scale attention mechanism for unsupervised remote sensing image generation. Experiments show that our method outperforms previous methods under several metrics. Visual and ablation results of attention layers show that Skip Attention has learned long-distance structural dependencies between similar targets.

关键词： Attention Mechanism remote sensing image Synthesis Generative Adversarial Network

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 << < 41 42 43 44 45 46 47 48 49 50 > >>

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：