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

3,560 篇 会议
37 篇 期刊文献
8 册 图书

馆藏范围

3,605 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,573 篇 工学
- 1,048 篇 电气工程
- 998 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
- 523 篇 软件工程
- 220 篇 光学工程
- 192 篇 仪器科学与技术
- 157 篇 电子科学与技术（可...
- 152 篇 控制科学与工程
- 150 篇 航空宇航科学与技...
- 90 篇 生物工程
- 82 篇 机械工程
- 63 篇 生物医学工程（可授...
- 62 篇 化学工程与技术
- 49 篇 建筑学
- 49 篇 环境科学与工程（可...
- 45 篇 土木工程
1,444 篇 理学
- 1,029 篇 物理学
- 244 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
- 30 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

1,087 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 hyperspectral im...
111 篇 image reconstruc...
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 beijing normal u...
15 篇 institute of rem...
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

3,546 篇 英文
31 篇 土耳其文
18 篇 其他
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 IX"

共 3605 条记录，以下是171-180 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：

Multi-scale Rotating Target Detection in High Resolution remote sensing image Based on Improved YOLOv5

Multi-scale Rotating Target Detection in High Resolution Rem...

引用

2023 International conference on image, signal processing, and Pattern Recognition, ISPP 2023

作者： Li, Jiapeng Wu, Jiaqi Liu, Qi Zhang, Zheng North China University of Technology Beijing100144 China China Institute of Water Resources and Hydropower Research Beijing100038 China

ISBN: (纸本)9781510666351

As the remote sensing image information rapidly becomes abundant, it is a challenge for the detection of tiny targets with dense distribution. Therefore, a multi-scale rotating object detection model based on the improved YOLOv5 is proposed in this paper. Firstly, because of adding a prediction feature layer to the network, the detection precision of tiny targets has substantially increased. Secondly, the loss between rotation anchors can be fitted at a high precision due to the compressed loss function aiming at the calculation of the IoU loss of rotation is proposed. Finally, a hybrid prior bounding box strategy is applied in the feature prediction layer to suit the targets to be detected in different sizes. Experiments conducted on the DOTA dataset indicates that this method significantly exceeded the original YOLOv5. It has extraordinary performances for the task of object detection in optical remote sensing image fields. © 2023 SPIE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Research on image Restoration in remote sensing Quick-View System 8th

Research on Image Restoration in Remote Sensing Quick-View S...

引用

8th International conference on signal and Information processing, Network and Computers, ICSINC 2021

作者： Wang, Yunsen Ma, Chengzhi Cui, Weiguang Liu, Jiang Kang, Yang Shandong Institute of Space Electronic Technology Shandong Yantai264670 China

ISBN: (纸本)9789811933868

Quick-view system plays an indispensable row in space exploration and earth observation. Currently, the remote sensing quick-view system of our country only has quick display and store abilities. The motion-blurred remote sensing images would not be restored. This paper centers on image restoration in remote sensing quick-view systems. Through calculating the two-level screw target matching image, the ratio of power spectrum density could be estimated quickly. Then Wiener filtering method is implemented to restore blurred images. To restrain the ringing artifact in quick-view system, different weights are introduced on the edge of the target when implementing Wiener filter. Experiments indicate that the optimized image restoration method can obtain relatively gratifying remote sensing data restorative results and treatment period, which might satisfy the requirement of real time display in remote sensing quick-view system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Compressed Domain Classification of remote sensing Scene images Based on Sub-band Data Fusion 28

Compressed Domain Classification of Remote Sensing Scene Ima...

引用

conference on image and signal processing for remote sensing XXVIII

作者： Arican, Berk Akkul, Erdem Safa Carkacioglu, Levent Toreyin, Behcet Ugur Istanbul Tech Univ Fac Comp & Informat Dept Comp Engn TR-34467 Istanbul Turkey Istanbul Tech Univ Informat Inst TR-34467 Istanbul Turkey ASELSAN AS TR-06200 Ankara Turkey

ISBN: (纸本)9781510655386;9781510655379

Storing and processing remote sensing (RS) images require large amounts of memory space and computing resources. Consequently, RS images are compressed and stored in various compression formats, such as JPEG2000. However, the processing of RS images for machine interpretation and understanding still necessitates the deployment of an image decompression stage in its entirety, followed by a computationally demanding image analysis pipeline. The image analysis stage is commonly composed of machine learning techniques, such as Deep Convolutional Neural Network (DCNN) models. Classification of remote sensing images is among the most common image analysis tasks. In the scope of this paper, we propose a sub-band image based classification method for the remote sensing Scene Classification (RSSC) task in the JPEG2000 compressed domain. The proposed approach exploits the already available sub-band image coefficients to classify RS images without needing for full decompression. Our study shows that our method increases the high frequency information in the LL sub-band and allows the image to contain more detail, leading to improved classifier performance while taking advantage of the partial decompression method.

关键词： JPEG2000 remote sensing Scene Classification Compressed Domain

来源：评论

学校读者我要写书评

暂无评论

BUILDING LANE-LEVEL MAPS FROM AERIAL imageS 49

BUILDING LANE-LEVEL MAPS FROM AERIAL IMAGES

引用

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

作者： Yao, Jiawei Pan, Xiaochao Wu, Tong Zhang, Xiaofeng Univ Washington Seattle WA 98195 USA Taiyuan Univ Technol Taiyuan Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Detecting lane lines from sensors is becoming an increasingly significant part of autonomous driving systems. However, less development has been made on high-definition lane-level mapping based on aerial images, which could automatically build and update offline maps for auto-driving systems. To this end, our work focuses on extracting fine-level detailed lane lines together with their topological structures. This task is challenging since it requires large amounts of data covering different lane types, terrain and regions. In this paper, we introduce for the first time a large-scale aerial image dataset built for lane detection, with high-quality polyline lane annotations on high-resolution images of around 80 kilometers of road. Moreover, we developed a baseline deep learning lane detection method from aerial images, called AerialLaneNet, consisting of two stages. The first stage is to produce coarsegrained results at point level, and the second stage exploits the coarse-grained results and feature to perform the vertexmatching task, producing fine-grained lanes with topology. The experiments show our approach achieves significant improvement compared with the state-of-the-art methods on our new dataset. Our code and new dataset are available at https://***/Jiawei-Yao0812/AerialLaneNet.

关键词： Computer Vision Lane Detection remote sensing

来源：评论

学校读者我要写书评

暂无评论

Numba-based Non-local Means Filter for SAR image Denoising 16

Numba-based Non-local Means Filter for SAR Image Denoising

引用

16th International conference on Computer and Automation Engineering (ICCAE)

作者： Devendhar, G. Singh, Pratibha Sharma, Rakesh Natl Inst Technol Dept Elect & Commun Engn Hamirpur Himachal Prades India

ISBN: (纸本)9798350370058;9798350370164

Synthetic Aperture Radar (SAR) images are an essential tool for earth observation and remote sensing, but speckle can degrade the quality of such images. image processing techniques such as filtering and averaging can effectively reduce the impact of noise on images. This paper evaluates the performance of the hybrid nonlocal means (NLM) filter in denoising Sentinel-1 SAR images. The proposed approach combines adaptive exponential kernel-based pre-processing with an NLM filter. The NLM algorithm is computationally intensive and time-consuming due to its pixel-by-pixel denoising based on extended neighborhood similarities. To address this challenge, this paper explores parallelization of the NLM algorithm using NUMBA, a Python compiler, to accelerate its performance. It is found that the proposed approach shows a significant improvement in terms of Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Squared Error (MSE) and performs better than conventional NLM filters and even provides the greatest speedup when compared to earlier parallel NLM techniques.

关键词： NLM denoising Multi-processing Parallelization Numba SMP

来源：评论

学校读者我要写书评

暂无评论

High-resolution sliding-spotlight spaceborne SAR imaging algorithm for Ka-band

High-resolution sliding-spotlight spaceborne SAR imaging alg...

引用

IET International Radar conference 2023, IRC 2023

作者： Xiao, Feng Jing, Maoqiang He, Dehua Zhang, Guo Zu, JiaGuo Yu, SongBai Sun, HanWei Wang, XinMin Beijing Institute of Radio Measurement Beijing China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing of Wuhan University Wuhan China Beijing China

ISBN: (纸本)9781839539954

High-band spaceborne SAR offers image-like optics and broadens the application range of microwave remote sensing on space-based platforms. The sliding-spotlight mode fulfils the high-resolution detection requirements for target recognition in remote sensing and has gained significant attention and application in the industry. Given the differences between high-band spaceborne SAR data processing and traditional low-band spaceborne SAR data processing in terms of orbital accuracy analysis, key parameter calculation of imaging, algorithm acceleration, and parallelization, this study conducts an analysis of data accuracy, imaging, and image quality using real data from Luojia-2. The results demonstrate that the proposed imaging process method achieves high-precision imaging of the sliding-spotlight SAR real data from Luojia-2. © The Institution of Engineering & Technology 2023.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Anomaly Interpretation Method Based on Data Mining for remote sensing Satellite Payload 10th

Anomaly Interpretation Method Based on Data Mining for Remot...

引用

10th International conference on signal and Information processing, Network and Computers, ICSINC 2022

作者： You, Jia Lu, Chengzhi China Academy of Space Technology No. 104 Youyi Road Haidian District Beijing China

ISBN: (纸本)9789811999673

Because of the rapid development of earth observation technology and artificial intelligence technology, high-resolution optical imaging satellites have emerged continuously, and the era of remote sensing big data has arrived. Due to the limitations of huge image size, sparse target distribution, rich variation of target time domain and insufficient labeling training samples, the current automatic detection of abnormal targets in satellite remote sensing images is inefficient and easy to generate false alarms. This paper proposes a data mining-based joint interpretation method for remote sensing satellite load data, which realizes automatic and fast accurate interpretation of remote sensing image abnormal targets and significantly improves the efficiency of camera data quality assessment. It is the first time to realize a major technical leap from visual interpretation to machine full adaptive interpretation of remote sensing image data, which can provide reference for subsequent adaptive interpretation of remote sensing satellite images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for remote sensing image Dehazing Using Satellite-to-Ground NDVI Knowledge

SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remot...

引用

2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Liu, Zepeng Yang, Zhicheng Zhu, Mingye Wong, Andy Wei, Yibing Han, Mei Yu, Jun Lai, Jui-Hsin University of Science and Technology of China China Ping An Technology China PAII Inc. United States University of Wisconsin - Madison United States

ISBN: (纸本)9798350313154

image dehazing is a meaningful low-level computer vision task and can be applied to a variety of contexts. In our industrial deployment scenario based on remote sensing (RS) images, the quality of image dehazing directly affects the grade of our crop identification and growth monitoring products. However, the widely used peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) provide ambiguous visual interpretation. In this paper, we design a new objective metric for RS image dehazing evaluation. Our proposed metric leverages a ground-based phenology observation resource to calculate the vegetation index error between RS and ground images at a hazy date. Extensive experiments validate that our metric appropriately evaluates different dehazing models and is in line with human visual perception. © 2023 IEEE.

关键词： remote sensing

来源：评论

学校读者我要写书评

暂无评论

A Convolutional Neural Network Approach to the Detection of LC Transitions in Multi-annual Satellite image Time Series 29

A Convolutional Neural Network Approach to the Detection of ...

引用

conference on image and signal processing for remote sensing XXix

作者： Meshkini, Khatereh Bovolo, Francesca Bruzzone, Lorenzo Fdn Bruno Kessler Ctr Digital Soc Via Sommar 18 I-38123 Trento Italy Univ Trento Dept Engn & Comp Sci Via Sommar 5 I-38123 Povo Trento Italy

ISBN: (纸本)9781510666955;9781510666962

Recently, deep learning-based methods have been exploited to learn complex features from Satellite image Time Series (SITS) with superior spatial, spectral, and temporal resolution for the Land Cover Transition (LCT) analysis. However, in order to efficiently utilize High Resolution (HR) SITS for detecting LCTs, there is a need to tackle challenges related to a proper modelling of the LC behaviour and pertain to the intricacy of the temporally dense SITS. A novel LCT detection approach is presented that exploits a pretrained Three Dimensional (3D) Convolutional Neural Network (CNN) to simultaneously extract spatio-temporal information from multi-annual SITS to identify the LCTs. To highlight the changed pixels, a multi-feature hyper temporal difference feature vector is generated that properly provides intrinsic information of the LC trends in space and time. To distinguish different LCTs between two consecutive years for the changed pixels, a clustering process is performed that considers the temporal information of the difference hyper features to discriminate and understand the LCTs. The product is a map indicating the location of changed pixels and providing information about the type of LCTs. The preliminary analysis has been done over a region in Sahel - Africa with images acquired between 2015 and 2016. The proposed approach has been compared with another LCT detection approach using 2D CNN. Experimental results confirm the effectiveness of the proposed approach in detecting the LCTs.

关键词： Land Cover Transition Satellite image Time Series Deep Learning Convolutional Neural Network remote sensing

来源：评论

学校读者我要写书评

暂无评论

Spatio-Semantic Prompt guided Adaptive Segment Anything for remote sensing Change Detection

Spatio-Semantic Prompt guided Adaptive Segment Anything for ...

引用

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

作者： Hu, Shenglong Han, Zhidong Dong, Gang Liang, Lingyan Wen, Dongchao Zhang, Kaihua IEIT SYSTEMS Co. Ltd. China NUIST China

ISBN: (纸本)9798350368741

Existing leading remote sensing change detection (RSCD) often takes a semantic-agnostic learning paradigm, which uses a binary ground-truth mask as supervision for model training. Despite the demonstrated success, due to the intrinsic characteristic of extremely complicated scene changes in RS images, this paradigm is prone to be misled by irrelevant semantic category changes, leading to a noisy CD mask prediction. To address this issue, this paper presents a Spatio-Semantic Prompt (SSP) guided adaptive Segment Anything Model (SAM) for RSCD, dubbed as SSP-SAM. The SSP-SAM introduces sparse textual and dense mask prompts into SAM to encode the task-specific semantic knowledge for RSCD. Specifically, we first encode the powerful textual semantic knowledge using Contrastive Language-image Pre-training (CLIP) to determine the desired change semantic category. Then, we design a spatial dense prompt module that yields an attention map as prompt features to further refine the desired changed regions. Subsequently, we fine-tune the SAM through an adaptor to integrate the spatial-semantic prompt cues, yielding a coarse CD mask prediction. Finally, guided by the coarse CD mask, a multi-scale mask attention mechanism is adopted to learn the refined semantic representations of the changed targets, predicting the accurate CD mask. Extensive experiments on a variety of benchmark datasets demonstrate that the proposed SSP-SAM achieves state-of-the-art performance. © 2025 IEEE.

关键词： contrastive language-image pre-training(CLIP) mask attention remote sensing change detection segment anything model(SAM) semantic information

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361页 << < 14 15 16 17 18 19 20 21 22 23 > >>

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：