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

3,541 篇 会议
35 篇 期刊文献
8 册 图书

馆藏范围

3,584 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,552 篇 工学
- 1,039 篇 电气工程
- 994 篇 计算机科学与技术...
- 893 篇 测绘科学与技术
- 712 篇 信息与通信工程
- 528 篇 软件工程
- 221 篇 光学工程
- 186 篇 仪器科学与技术
- 157 篇 控制科学与工程
- 156 篇 航空宇航科学与技...
- 155 篇 电子科学与技术（可...
- 91 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...
1,428 篇 理学
- 1,017 篇 物理学
- 246 篇 数学
- 102 篇 生物学
- 93 篇 系统科学
- 85 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

1,081 篇 remote sensing
202 篇 image processing
175 篇 feature extracti...
169 篇 image segmentati...
168 篇 synthetic apertu...
155 篇 satellites
138 篇 signal processin...
119 篇 image resolution
111 篇 image reconstruc...
111 篇 hyperspectral im...
105 篇 image classifica...
103 篇 image fusion
101 篇 deep learning
95 篇 image analysis
90 篇 spatial resoluti...
89 篇 radar imaging
88 篇 sensors
87 篇 signal processin...
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 chinese acad sci...
12 篇 beijing institut...
11 篇 univ chinese aca...
11 篇 school of inform...
11 篇 beijing key lab ...
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 bovolo francesca
17 篇 vozel benoit
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

3,492 篇 英文
51 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 XXVII"

共 3584 条记录，以下是71-80 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：

Real-time image Recognition System based on Ultra-Low-Latency Spiking Neural Networks 2

Real-time Image Recognition System based on Ultra-Low-Latenc...

引用

2nd IEEE International conference on signal, Information and Data processing, ICSIDP 2024

作者： Lan, Bin Zhang, Xi Dong, Heng Xu, Ming Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Innovation Equipment Research Institute Sichuan TianFu New Area Chengdu610299 China Beijing100081 China

ISBN: (纸本)9798331515669

remote sensing technology plays an important role in many tasks such as natural disaster detection, weather and climate monitoring and military defense. Currently, remote sensing image processing predominantly relies on Convolutional Neural Networks (CNNs) and Transformers, which require a huge amount of multiplication and addition operations, bringing pressure to the deployment of satellite platforms with strict power consumption and computing power constraints. Spiking neural network (SNN), where the signal is encoded as a spike train instead of analog values, significantly reduces energy consumption compared to traditional artificial neural network. This paper proposes an all-input parallel deployment approach that minimizes or eliminates interactions with off-chip memory, thereby accelerating inference speed and reducing power consumption. We deployed LeNet ultra-low latency SNN on the Xilinx VC709 Board. Experimental results demonstrate that the proposed accelerator achieves a processing speed of 1240 frames per second, an accuracy rate of 98.14%, and a power consumption of only 0.745 Watts, making it well-suited for the low power and real-time requirements of remote sensing tasks. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

SPATIAL-FREQUENCY NETWORK FOR SEGMENTATION OF remote sensing imageS 30

SPATIAL-FREQUENCY NETWORK FOR SEGMENTATION OF REMOTE SENSING...

引用

30th IEEE International conference on image processing (ICIP)

作者： Zhang, Tony Dick, Robert P. Univ Michigan Elect & Comp Engn Dept Ann Arbor MI 48109 USA

ISBN: (纸本)9781728198354

We describe a deep learning system for satellite image segmentation. Our CNN model embeds contextual feature dependencies in both spatial and frequency domains. Its Spatial Weighting Module uses a multi-scale pooling layer to represent correlations at longer length scales in the spatial domain. Its Frequency Weighting Module uses frequency-domain information to better discriminate between object classes. Experimental results on the Potsdam dataset demonstrate that our model has a 1.9% higher average F1 accuracy than previous methods.

关键词： remote sensing segmentation spatial frequency

来源：评论

学校读者我要写书评

暂无评论

HSDFormer: an improved transformer for remote sensing image semantic segmentation

HSDFormer: an improved transformer for remote sensing image ...

引用

2024 International conference on image, signal processing, and Pattern Recognition, ISPP 2024

作者： Bai, Yang Gao, Suixiang University of Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Zhongguancun Laboratory Beijing100049 China

ISBN: (纸本)9781510680425

The extraction of building locations is crucial in the field of remote sensing, commonly applied in tasks such as emergency response, urban planning, and environmental monitoring. Existing methods often employ convolutional models such as ResNet and U-Net. however, these models struggle to capture long-range features of geographical objects, limiting their practical effectiveness. Meanwhile, Transformer based models face challenges due to their quadratic computational complexity. Additionally, the characteristics of remote sensing images, including dense arrangement, small scale, and class imbalance, pose difficulties for traditional patch-merging modules in downsampling, leading to the loss of significant building information. To address such limitations, we present the HSDFormer model in this paper. The model achieves a reduction in the computational complexity of self-attention calculation through a straightforward sequential reduction process. Additionally, our model has optimized the downsampling process specifically for remote sensing semantic segmentation tasks, effectively minimizing the loss of crucial information during layer-wise feature extraction. We evaluate the HSDFormer on the WHU Building Dataset and Satellite Dataset II (East Asia). The results demonstrate the model's effectiveness in accurately segmenting building locations, surpassing performance compared to other baseline models. © 2024 SPIE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

TALDS: A Transfer-Active Learning-Driven Siamese Network for Bi-temporal image Classification 7

TALDS: A Transfer-Active Learning-Driven Siamese Network for...

引用

IEEE 7th International conference on Advanced Technologies, signal and image processing (ATSIP)

作者： Chouikhi, Farah ben Abbes, Ali Farah, Imed Riadh Univ Manouba Natl Sch Comp Sci Manouba 2010 Tunisia Univ Manouba Manouba Sch Engn Manouba 2010 Tunisia LESOR Inst Arid Reg Medenine 4119 Tunisia

ISBN: (纸本)9798350351491;9798350351484

A novel Transfer-Active Learning-Driven Siamese network for bi-temporal image classification (TALDS) is proposed. It incorporates transfer learning (TL) and active learning (AL) techniques to facilitate the selection of informative samples in an iterative process. This approach allows the model to learn from different domains efficiently, improving its accuracy and robustness. TALDS network shows promising results for bi-temporal image classification tasks and is therefore a valuable contribution to computer vision. The Siamese network architecture enables the network to learn to extract coherent features from bi-temporal images, allowing for accurate image classification. Experimental results demonstrate the effectiveness of our framework in detecting desertification using satellite images. The proposed method offers numerous possibilities for implementation in different domains, including environmental monitoring and remote sensing.

关键词： image classification Siamese Variational AutoEncoder Transfer Learning Active Learning Artificial Intelligence Change detection

来源：评论

学校读者我要写书评

暂无评论

remote sensing aircraft small object detection algorithm based on YOLOv5

Remote sensing aircraft small object detection algorithm bas...

引用

2024 International conference on image, signal processing, and Pattern Recognition, ISPP 2024

作者： Qiu, Yijuan Xue, Jiefeng Zhang, Jie Jiang, Ping Zhang, Gang Lei, Tao National Key Laboratory of Optical Field Manipulation Science and Technology Chinese Academy of Sciences Sichuan Chengdu610209 China Institute of Optics and Electronics Chinese Academy of Sciences Sichuan Chengdu610209 China University of Chinese Academy of Sciences Beijing100049 China Southwest University of Science and Technology 621010 China

ISBN: (纸本)9781510680425

With the rapid development of remote sensing technology, remote sensing images play an important role in the agricultural field, geological field, and natural disaster detection. The size of aircraft in complex scenes in remote sensing images is extremely small, and aircraft of different models have similar shapes. Therefore, improving the accuracy of aircraft target recognition is a challenging task. We propose an improved aircraft small target recognition method based on yolov5, which can improve the recognition accuracy of aircraft targets while ensuring the speed of the model. The specific content is as follows: To address the problem of lack of remote sensing aircraft training sets, we use existing public remote sensing images to combine with aircraft model images. Most of the aircraft models only occupy a dozen to twenty pixels in the 1k*1k image, and perform Scale the generated data set;in order to better combine features of different scales and obtain higher-level feature fusion, introduce the BiFPN module with more residual connections and more complete feature fusion;use the SE attention mechanism to learn the weights of different features, extract information that is more important for detection and improve model performance;in view of the small size of the aircraft, a detection method based on Wasserstein distance such as NWD (Normalized Wasserstein Distance) is selected as the loss function. © 2024 SPIE.

关键词： Training aircraft

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 2

Physically Plausible Spectral Reconstruction in Remote Sensi...

引用

2nd IEEE International conference on signal, Information and Data processing, ICSIDP 2024

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

ISBN: (纸本)9798331515669

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. © 2024 IEEE.

关键词： Optical remote sensing

来源：评论

学校读者我要写书评

暂无评论

D-CANet: Diverse class-aware coding and decoding structure network for semantic segmentation of high-resolution remote sensing images

D-CANet: Diverse class-aware coding and decoding structure n...

引用

2024 International conference on image, signal processing, and Pattern Recognition, ISPP 2024

作者： Yuan, Zhengwu Shao, Wen Chen, Qiang Ke, Yingqi College of Computer Science and Technology Chongqing University of Posts and Telecommunication Chongqing China

ISBN: (纸本)9781510680425

The substantial scale variation and intra-class diversity within remote sensing imagery pose significant challenges for semantic segmentation, rendering methods developed for natural images inapplicable. These challenges, we introduce a novel semantic segmentation model named D-CANet, which primarily comprises three modules: the Global Class Center Awareness (GCCA), the Local Class Awareness Module (LCAM), and the Global Class Generation Module (GCG). Specifically, the GCCA module is dedicated to modeling the global representation of class context to mitigate the interference from image backgrounds;the LCAM module generates a local class representation, serving as an intermediary perceptual element that facilitates an implicit linkage between pixels and global class representations, minimizing the variance within classes;following the processing by the LCAM module, the GCG module enhances the global class representation. This encoder-decoder structure equipped with GCCA, LCAM, and GCG modules achieves precise segmentation of objects of varying scales within remote sensing imagery through the interactive perception and fusion of global and local features. Experimental assessments conducted on the Potsdam dataset and the Vaihingen dataset illustrate that D-CANet surpasses the current state-of-the-art semantic segmentation techniques in terms of efficacy. © 2024 SPIE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

M2F2Net: Multi-stage Mixed Feature Fusion Network For remote sensing Change Detection

M2F2Net: Multi-stage Mixed Feature Fusion Network For Remote...

引用

2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Gu, Binhao Song, Lei Gu, Binjie Kong, Youyong Southeast University Nanjing China Jiangnan University Wuxi China

ISBN: (纸本)9798350368741

remote sensing image change detection (CD) seeks to analyze and discern changes in surface objects through the use of multi-temporal remote sensing imagery. However, as image resolution advances, existing methods often fall short in capturing comprehensive visual feature representations, and their networks are prone to spatial degradation. This results in incomplete boundary detection and difficulties in identifying subtle changes. To overcome these challenges, this paper introduces a Siamese U-Net architecture incorporating Multistage Mixed Feature Fusion (M2F2Net). The proposed model leverages a convolutional neural network (CNN) as the main encoder for local feature extraction, while employing a Transformer-based auxiliary encoder to capture global features. Furthermore, we introduce a Feature Fusion Module (FFM) to facilitate the efficient integration of local and global information. Additionally, we propose a novel convolutional unit and a Spatial Attention Module (SAM) designed to enhance the extraction of image features. Experimental results confirm that the proposed approach delivers substantial improvements across multiple evaluation criteria, while offering a superior accuracy compared to existing state-of-the-art change detection methods. © 2025 IEEE.

关键词： Change detection (CD) Deep learning Multi scale remote sensing images Self attention mechanism

来源：评论

学校读者我要写书评

暂无评论

Seeing the World through an Antenna's Eye: Reception Quality Visualization Using Incomplete Technical signal Information 32

Seeing the World through an Antenna's Eye: Reception Quality...

引用

32nd European signal processing conference (EUSIPCO)

作者： Bergerhoff, Leif German Aerosp Ctr DLR German Remote Sensing Data Ctr DFD Natl Ground Segment NBS Neustrelitz Germany

ISBN: (纸本)9789464593617;9798331519773

We come up with a novel application for image analysis methods in the context of direction dependent signal characteristics. For this purpose, we describe an inpainting approach adding benefit to technical signal information which are typically only used for monitoring and control purposes in ground station operations. Recalling the theoretical properties of the employed inpainting technique and appropriate modeling allow us to demonstrate the usefulness of our approach for satellite data reception quality assessment. In our application, we show the advantages of inpainting products over raw data as well as the rich potential of the visualization of technical signal information.

关键词： signal information satellite data reception inpainting homogeneous diffusion partial differential equations data analysis modeling application

来源：评论

学校读者我要写书评

暂无评论

SAR2NDVI: PRE-TRAINING FOR SAR-TO-NDVI image TRANSLATION 49

SAR2NDVI: PRE-TRAINING FOR SAR-TO-NDVI IMAGE TRANSLATION

引用

49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Kimura, Daiki Ishikawa, Tatsuya Mitsugi, Masanori Kitakoshi, Yasunori Tanaka, Takahiro Simumba, Naomi Tanaka, Kentaro Wakabayashi, Hiroaki Sampei, Masato Tatsubori, Michiaki IBM Res Yorktown Hts NY 10598 USA IBM Japan Ltd Minato City Tokyo Japan IBM Japan Digital Serv Co Tokyo Japan Space Shift Inc Tokyo Tokyo Japan

ISBN: (纸本)9798350344868;9798350344851

Geospatial machine learning is of growing importance in various global remote-sensing applications, particularly in the realm of vegetation monitoring. However, acquiring accurate ground truth data for geospatial tasks remains a significant challenge, often entailing considerable time and effort. Foundation models, emphasizing pre-training on large-scale data and fine-tuning, show promise but face limitations when applied to geospatial data due to domain differences. Our paper introduces a novel image translation method, combining geospatial-specific pre-training with training and test-time data augmentation. In a case study involving the translation of normalized difference vegetation index (NDVI) values from synthetic aperture radar (SAR) images of cabbage farms, our approach outperformed competitors by 31% in a public competition. It also exceeded the average of the top five teams by 44%. We publish both our image translation method with baseline methods and the geospatial-specific dataset at https://***/IBM/SAR2NDVI.

关键词： Geospatial image translation Pre-training

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 << < 4 5 6 7 8 9 10 11 12 13 > >>

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：