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

662 篇 会议
23 册 图书
22 篇 期刊文献

馆藏范围

707 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

500 篇 工学
- 332 篇 计算机科学与技术...
- 161 篇 软件工程
- 96 篇 测绘科学与技术
- 93 篇 电气工程
- 88 篇 信息与通信工程
- 71 篇 光学工程
- 52 篇 控制科学与工程
- 28 篇 建筑学
- 28 篇 土木工程
- 27 篇 化学工程与技术
- 27 篇 航空宇航科学与技...
- 26 篇 机械工程
- 23 篇 仪器科学与技术
- 22 篇 生物工程
- 21 篇 电子科学与技术（可...
- 19 篇 生物医学工程（可授...
- 15 篇 环境科学与工程（可...
- 12 篇 交通运输工程
- 12 篇 农业工程
225 篇 理学
- 128 篇 物理学
- 83 篇 数学
- 26 篇 化学
- 26 篇 统计学（可授理学、...
- 24 篇 生物学
- 10 篇 地理学
- 10 篇 地球物理学
127 篇 医学
- 125 篇 临床医学
60 篇 管理学
- 43 篇 图书情报与档案管...
- 20 篇 管理科学与工程(可...
15 篇 农学
- 13 篇 作物学
6 篇 军事学
6 篇 艺术学
3 篇 经济学
2 篇 法学
1 篇 教育学
1 篇 文学

主题

194 篇 remote sensing
105 篇 pattern recognit...
78 篇 image processing
37 篇 image segmentati...
31 篇 feature extracti...
25 篇 artificial intel...
25 篇 satellites
19 篇 object detection
19 篇 machine learning
18 篇 training
17 篇 image fusion
17 篇 data mining
17 篇 image analysis
16 篇 image classifica...
16 篇 computer vision
16 篇 image resolution
14 篇 image edge detec...
14 篇 robustness
13 篇 deep learning
13 篇 remote sensing i...

机构

6 篇 tsinghua univers...
6 篇 image processing...
5 篇 university of te...
5 篇 indian institute...
5 篇 dalian universit...
5 篇 university of sy...
5 篇 institute of aut...
5 篇 institute of aut...
5 篇 shenzhen univers...
5 篇 college of infor...
4 篇 institute of ima...
4 篇 sun yat-sen univ...
4 篇 hong kong baptis...
4 篇 southern univers...
3 篇 univ arizona col...
3 篇 mks imaging tech...
3 篇 university of el...
3 篇 peking univ inst...
3 篇 indian institute...
3 篇 national laborat...

作者

8 篇 anon
8 篇 chunhong pan
7 篇 ping guo
5 篇 zhaoxiang zhang
5 篇 hui huang
5 篇 balasubramanian ...
5 篇 jing dong
5 篇 tieniu tan
5 篇 jiwen lu
5 篇 risheng liu
5 篇 wanli ouyang
5 篇 min xu
5 篇 huchuan lu
4 篇 qian yin
4 篇 junwei han
4 篇 jianhuang lai
4 篇 pan chunhong
4 篇 zhang jie
4 篇 yike guo
4 篇 jianguo zhang

语言

688 篇 英文
15 篇 其他
4 篇 中文

检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing II"

共 707 条记录，以下是41-50 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：

Application of Band Combination in Landslide Identification of remote sensing images Driven by Deep Learning 2

Application of Band Combination in Landslide Identification ...

引用

2nd International conference on image processing, Computer Vision and Machine Learning, ICICML 2023

作者： Yang, Tianyu Xu, Weijie Hua, Yu Chengdu University of Technology College of Computer Science and Cyber Security Oxford Brookes College Chengdu China

ISBN: (纸本)9798350331417

With the frequent occurrence of geological disasters, landslide identification has become an important research problem. In recent years, with the application and research of deep learning in the fields of computer vision and remote sensing image analysis, deep learning has become a popular method for landslide identification research because of its excellent feature extraction and pattern recognition capabilities. In this paper, we add new bands reflecting vegetation and water of landslide factors to the original bands of remote sensing image samples by linear combination of bands, and conduct two sets of comparison experiments on Unet and Swin-Unet to verify that adding band features can help improve landslide identification accuracy. The experimental results show that the experimental group with additional band features has a numerical improvement in landslide identification accuracy, with F1-score as the evaluation index, the Unet experimental group has a 2.29% improvement and the Swin-Unet experimental group has a 1.78% improvement. The results of this paper have implications for the subsequent application of bands combination of landslide factor features in remote sensing landslide identification driven by deep learning methods. © 2023 IEEE.

关键词： deep learning landslide identification linear combination of bands remote sensing

来源：评论

学校读者我要写书评

暂无评论

Diffusion Posterior Sampling for remote sensing image Fusion 7

Diffusion Posterior Sampling for Remote Sensing Image Fusion

引用

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

作者： Zhang, Chenlin Wang, Zelong Han, Jialing College of science National University of Defense Technology Changsha China

ISBN: (纸本)9781839539848

remote sensing image fusion, i.e., fusing remote sensing images from different sensors or different time into a comprehensive image, can integrate image information for all kinds of image tasks, such as object detection, pattern recognition, and so on. In this paper, we focus on the image fusion of optical image and synthetic aperture radar (SAR) image, where the traditional methods, such as sparse representation and image decomposition, usually fail to improve the image quality and enhance object features’ level. Inspired by the powerful generation model named diffusion denoising probabilistic model (DDPM), we propose a novel method based on diffusion posterior sampling for image fusion of optical image and SAR image. Starting from the variational model of image fusion and the total variation constraint, we approximate the posterior sampling process of image fusion by a closed-form analytic solution. After that, DDPM can be used to generate the fused image with high image quality and enhanced object features. The feasibility and the superiority of the proposed method is validated in numerical experiments. © The Institution of Engineering & Technology 2023.

关键词： image fusion

来源：评论

学校读者我要写书评

暂无评论

Adaptive Enhanced Reversible Flow Model for remote sensing image Super Resolution 27th

Adaptive Enhanced Reversible Flow Model for Remote Sensing I...

引用

27th International conference on pattern recognition, ICPR 2024

作者： Li, Peishan Zhang, Yonghong Wang, Junfei Ma, Guangyi Yuan, Ziwei Nanjing University of Information Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9783031781186

In recent years, convolutional neural networks (CNNs) have excelled in remote sensing image super-resolution reconstruction (RSISR) tasks, becoming the predominant algorithms in this domain. However, these models primarily leverage the dependency of high-resolution (HR) images on low-resolution (LR) counterparts during the super-resolution (SR) forward process, neglecting mutual dependencies. To address the ill-posed nature of one-to-many mappings and enhance reconstruction performance, this paper proposes Adaptive Enhanced Reversible Flow Model (AERNet), an image SR algorithm based on invertible neural networks. AERNet treats image degradation and reconstruction as invertible transformations, where LR and HR images mutually project into each other's spaces. This mutual dependency optimizes distribution mapping across LR and HR images, constraining the solution space effectively in both forward and inverse directions. Integrating a multi-path adaptive feature fusion group and a global interaction enhancement module enhances the network's adaptability, improving its capability to fuse and enhance feature information. This approach enables more accurate processing of key image details and regions. Experimental results demonstrate AERNet's superior performance on two benchmark remote sensing datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Grassland botanical species identification based on MTMF model

Grassland botanical species identification based on MTMF mod...

引用

2023 International conference on image, Signal processing, and pattern recognition, ISPP 2023

作者： Huang, Mengge Wang, Xinhong Ma, Lingling Ouyang, Guangzhou Liu, Wei Zhu, Xiaohua Niu, Yifang Tang, Lingli Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Quantitative Remote Sensing Information Technology Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Science Beijing100049 China Institute of Botany Chinese Academy of Sciences Beijing100093 China

ISBN: (纸本)9781510666351

Grassland plays an important role in regional economic development and terrestrial ecosystem security. remote sensing technology is fast, efficient and low cost. The use of remote sensing technology to discriminate grassland species is an important way to monitor the population dynamics and botanical community succession in grassland. Such information is conducive to the timely and accurate detection of changes in the grassland ecological environment and provides an important reference for the scientific management of grassland ecosystems and the construction of an ecologically aware civilization. In this paper, based on the UAV hyperspectral remote sensing image of natural grassland in Inner Mongolia and the linear spectral mixture model, the MTMF (mixture tuned matched filtering) model was used for grass species identification, and the results were verified with FVC (fractional vegetation cover) estimation. The results showed that this method could achieve effective identification and extraction of the dominant species of Leymus chinensis in the study area. © 2023 SPIE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

A temporal-spatial feature coordination network for change detection of VHR images 3

A temporal-spatial feature coordination network for change d...

引用

3rd International conference on Geographic Information and remote sensing Technology, GIRST 2024

作者： Zeng, Yuxian Zhang, Xinxin College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781510689077

Deep learning techniques have significantly improved the accuracy and efficiency of change detection of very high resolution (VHR) images. However, many current models ignore the inherent heterogeneity of bi-temporal remote sensing images, thus making it difficult to distinguish between target changes and background variations. To address this problem, we propose a deep learning (DL) framework, namely temporal-spatial feature coordination network (TSFCN), to improve the accuracy of detection by enhancing the differentiation of change pixel features and improving the extraction and processing of change information. Further comparison and ablation experiments on the SVCD dataset show that the proposed model, compared with other models, improves the F1 scores and IoU by 2.09% and 3.81%, respectively. The results demonstrate that our model can achieve better accuracy and performance improvement. © 2025 SPIE.

关键词： Change detection

来源：评论

学校读者我要写书评

暂无评论

Probability-based Global Cross-modal Upsampling for Pansharpening

Probability-based Global Cross-modal Upsampling for Pansharp...

引用

IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Zhu, Zeyu Cao, Xiangyong Zhou, Man Huang, Junhao Meng, Deyu Xi An Jiao Tong Univ Xian Peoples R China Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Peoples R China Xi An Jiao Tong Univ Minist Educ Key Lab Intelligent Networks & Network Secur Xian Peoples R China Macau Univ Sci & Technol Taipa Macao Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350301298

Pansharpening is an essential preprocessing step for remote sensing image processing. Although deep learning (DL) approaches performed well on this task, current up-sampling methods used in these approaches only utilize the local information of each pixel in the low-resolution multi-spectral (LRMS) image while neglecting to exploit its global information as well as the cross-modal information of the guiding panchromatic (PAN) image, which limits their performance improvement. To address this issue, this paper develops a novel probability-based global cross-modal up-sampling (PGCU) method for pan-sharpening. Precisely, we first formulate the PGCU method from a probabilistic perspective and then design an efficient network module to implement it by fully utilizing the information mentioned above while simultaneously considering the channel specificity. The PGCU module consists of three blocks, i.e., information extraction (IE), distribution and expectation estimation (DEE), and fine adjustment (FA). Extensive experiments verify the superiority of the PGCU method compared with other popular upsampling methods. Additionally, experiments also show that the PGCU module can help improve the performance of existing SOTA deep learning pansharpening methods. The codes are available at https://***/Zeyu-Zhu/PGCU.

关键词： Photogrammetry and remote sensing

来源：评论

学校读者我要写书评

暂无评论

CSVD: a cross-scenario vehicle dataset for multi-object tracking

CSVD: a cross-scenario vehicle dataset for multi-object trac...

引用

2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Li, Xiaolei Zhou, Juefan Xiao, Xingjie Lin, Jiayu Yang, Siyuan Sha, Zongyao Tu, Jianguang School of Remote Sensing and Information Engineering Wuhan University Hubei Province 430079 China Wuhan ALLPRS Remote Sensing Data Technology Co. Ltd. Hubei Province 430079 China

ISBN: (纸本)9781510680425

Leveraging visual sensing technologies for the detection and tracking of vehicles represents a critical application domain for unmanned aerial vehicles (UAVs), notably in challenging operational *** study focuses on enhancing UAV functionalities in intricate environments through the development of a specialized dataset, derived from battlefield scenarios, to facilitate advanced research on vehicle detection and multi-target tracking under complex conditions.A comprehensive collection of vehicular movement videos spanning diverse scenarios was amassed and manually annotated, culminating in the creation of the " Cross-Scenario Vehicle Detection" (CSVD) *** dataset encompasses a wide array of environmental settings, featuring urban landscapes, plains, and forests, across the four seasons, resulting in a total of 13,025 meticulously annotated *** several state-of-the-art deep learning models, we established robust benchmarks for object ***, an extensive evaluation and performance validation were conducted using cutting-edge multi-object tracking algorithms on the CSVD dataset, incorporating diverse assessment *** conducted experiments demonstrate the dataset's robust applicability and versatility, endorsing its effectiveness for the development and evaluation of UAV-based vehicle detection and multi-target tracking systems in complex settings. © 2024 SPIE.

关键词： Aircraft detection

来源：评论

学校读者我要写书评

暂无评论

remote sensing image Object Detection Method with Feature Denoising Fusion Module 7

Remote Sensing Image Object Detection Method with Feature De...

引用

7th IEEE Advanced Information Technology, Electronic and Automation Control conference, IAEAC 2024

作者： Chen, Penghui Li, Qishen Li, Qiufeng Wu, Zhongyu Nanchang Hangkong University School of Information Engineering Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China Nanchang Hangkong University School of Software Jiangxi Nanchang China Jiangxi Nanchang China

ISBN: (纸本)9798350339161

remote sensing object detection is an important research area in computer vision, widely applied in both military and civilian domains. However, challenges in remote sensing image object detection such as large image sizes, complex backgrounds, and significant variations in target scales are prevalent. To address these issues, this paper proposes a new Feature Denoising and Fusion Module (FDFM) aimed at enhancing the accuracy and robustness of object detection. This module comprises a Multi-Scale Denoising Submodule(MDS) and an Attention Optimization Submodule(AOS). The Multi-Scale Denoising Module aims to suppress lower-level texture noise by utilizing higher-level semantic features before the fusion process, reducing the impact of lower-level noise on subsequent multi-scale feature fusion. Meanwhile, the Attention Optimization Module seeks to enhance the precision of self-attention computations within the Multi-Scale Denoising Module without increasing the parameter count. The efficacy of this method was evaluated on public datasets DOTA, VisDrone, VOC and COCO, showing improvements in comparison to baseline models. © 2024 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

RTMDet-R2: An Improved Real-Time Rotated Object Detector 6th

RTMDet-R2: An Improved Real-Time Rotated Object Detector

引用

6th Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Xiang, Haifeng Jing, Naifeng Jiang, Jianfei Guo, Hongbo Sheng, Weiguang Mao, Zhigang Wang, Qin Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9789819985548;9789819985555

Object detection in remote sensing images is challenging due to the absence of visible features and variations in object orientation. Efficient detection of objects in such images can be achieved using rotated object detectors that utilize oriented bounding boxes. However, existing rotated object detectors often struggle to maintain high accuracy while processing high-resolution remote sensing images in real time. In this paper, we present RTMDet-R2, an improved real-time rotated object detector. RTMDet-R2 incorporates an enhanced path PAFPN to effectively fuse multi-level features and employs a task interaction decouple head to alleviate the imbalance between regression and classification tasks. To further enhance performance, we propose the ProbIoU-aware dynamic label assignment strategy, which enables efficient and accurate label assignment during the training. As a result, RTMDet-R2-m and RTMDet-R2-l achieve 79.10% and 79.46% mAP, respectively, on the DOTA 1.0 dataset using single-scale training and testing, outperforming the majority of other rotated object detectors. Moreover, RTMDet-R2-s and RTMDet-R2-t achieve 78.43% and 77.27% mAP, respectively, while achieving inference frame rates of 175 and 181 FPS at a resolution of 1024 x 1024 on an RTX 3090 GPU with TensorRT and FP16-precision. Furthermore, RTMDet-R2-t achieves 90.63/97.44% mAP on the HRSC2016 dataset. The code and models are available at https://***/Zeba-Xie/RTMDet-R2.

关键词： remote sensing images Object Detection Rotated Object Detection Feature Fusion Label Assignment

来源：评论

学校读者我要写书评

暂无评论

Stacking algorithm based on naive Bayes 2

Stacking algorithm based on naive Bayes

引用

2nd Asia conference on Computer Vision, image processing and pattern recognition (CVIPPR)

作者： Huang, Chen Zhou, Yuting Yang, Xuemei Liu, Shiqi Yin, Junping Northeast Normal Univ Acad Adv Interdisciplinary Studies Jilin Jilin Peoples R China Beijing Inst Remote Sensing Beijing Peoples R China China Acad Engn Phys Computat Math Mianyang Sichuan Peoples R China Beijing Inst Appl Phys & Computat Math Big Data Ctr Beijing Peoples R China

ISBN: (纸本)9798400716607

With the advent of the data era, medical image data plays an increasingly important role in diagnosing and treating diseases. This study combined deep learning, ensemble learning, and mathematical theory to construct a Stacking algorithm model based on the Naive Bayes method to achieve more accurate classification of medical images. In the Stacking algorithm, the results of the primary classifier are treated equally, and the performance of the primary classifier is ignored. The poor performance of the primary classifier will affect the final classification result. To solve this problem, a Stacking algorithm based on the Naive Bayes method is proposed in this paper. Firstly, the performance of the primary classifier is evaluated, and the output of the primary classifier is selected reasonably through the evaluation results. Secondly, the VGGNet, InceptionNet, and ResNet convolutional networks were used as primary classifiers to construct a complete Stacking algorithm based on the Naive Bayes method. Next, the algorithm was tested and validated using COVID-19 and NoCOVID-19 lung CT image data. Finally, it is compared with a single primary classifier and the traditional Stacking algorithm. The experimental results show that the Stacking algorithm based on the Naive Bayes method is suitable for the binary classification task of medical images and has better classification results than a single primary classifier and the traditional Stacking algorithm.

关键词： Stacking algorithm Naive Bayes method Convolutional neural network Medical image

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71页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：