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511-520 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：

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

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

引用

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

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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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.

关键词： Training Adaptation models Accuracy Semantics Benchmark testing signal processing Sensors Noise measurement Speech processing remote sensing

来源：评论

学校读者我要写书评

暂无评论

SINGLE remote sensing image DEHAZING USING A DUAL-STEP CASCADED RESIDUAL DENSE NETWORK

SINGLE REMOTE SENSING IMAGE DEHAZING USING A DUAL-STEP CASCA...

引用

IEEE International conference on image processing (ICIP)

作者： Huang, Yufeng Chen, Xiang Shenyang Aerosp Univ Coll Elect & Informat Engn Shenyang Peoples R China

ISBN: (纸本)9781665441155

remote sensing (RS) dehazing is an extremely challenging task since the non-uniform distribution of haze and fog severely degrade the images and difficult to extract features. To address these issues, we propose an end to end Dual-step Cascaded Residual Dense Network called DCRD-Net, which can exactly remove haze from the hazy RS image and precisely restoring the details. The architecture of network contains two cascaded task-driven subnetworks, in order to deal with the coarse and fine haze-relevant features separately. Besides, the Residual Dense Enhancement Block ( RDEB) is involved to guide the feature extraction, so that multi-scale information can be used to estimate the local and global features. Further, the Squeeze and Excitation (SE) Block is employed to optimize the RDEB, for the purpose to get the contextual feature and reduce computation complex. Quantitative and qualitative results illustrate that the designed framework outperforms the recent outstanding dehazing methods on promoting haze remove and restoring detailed in the synthetic and real-world RS images under various scenes. To encourage more comparisons, we release our codes on GitHub https://***/cxtalk /DCRD-Net.

关键词： Residual Dense Enhancement Block image dehazing remote sensing image cascade network

来源：评论

学校读者我要写书评

暂无评论

image Restoration Based on Blur Kernel Estimation Using Vibration Data

Image Restoration Based on Blur Kernel Estimation Using Vibr...

引用

IEEE International conference on signal and image processing (ICSIP)

作者： Weida Xing Weixiao Tuo Xingfei Li Chenxi Yang State Key Laboratory of Precision Measuring Technology and Instruments Tianjin University Tianjin China Taihu Laboratory of Deepsea Technological Science Wuxi China

remote sensing images are usually blurred due to platform vibrations and camera defocus, which seriously limit its application. To solve the problem of blurring remote sensing images caused by in-orbit angular vibration, an image restoration approach is proposed. Magnetohydrodynamics (MHD) angular rate sensor is used to measure the angular vibration experiencing by the camera. The blur kernel is estimated with detected vibration and defocusing radius. A regularized image deblurring algorithm with total variation (TV) regularization is used to restore the blurred image. The modulation transfer function (MTF) is used to evaluate quality of the restored image. Simulations and experiments are conducted to validate the proposed approach. Results indicate that the proposed method can improve the image quality while preserving the detail information.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Koopman Ensembles for Probabilistic Time Series Forecasting

Koopman Ensembles for Probabilistic Time Series Forecasting

引用

European signal processing conference (EUSIPCO)

作者： Anthony Frion Lucas Drumetz Guillaume Tochon Mauro Dalla Mura Abdeldjalil Aissa El Bey Lab-STICC IMT Atlantique Brest France LRE EPITA Le Kremlin-Bicêtre France CNRS Grenoble INP GIPSA-lab Univ. Grenoble Alpes Institut Universitaire de France Grenoble France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

In the context of an increasing popularity of data-driven models to represent dynamical systems, many machine learning-based implementations of the Koopman operator have recently been proposed. However, the vast majority of those works are limited to deterministic predictions, while the knowledge of uncertainty is critical in fields like meteorology and climatology. In this work, we investigate the training of ensembles of models to produce stochastic outputs. We show through experiments on real remote sensing image time series that ensembles of independently trained models are highly overconfident and that using a training criterion that explicitly encourages the members to produce predictions with high inter-model variances greatly improves the uncertainty quantification of the ensembles.

关键词： Training Uncertainty Time series analysis Stochastic processes Predictive models signal processing Probabilistic logic Sensors Forecasting remote sensing

来源：评论

学校读者我要写书评

暂无评论

PSC-FPN: Pixel Shuffling Algorithm for Channel Conversion in Feature Pyramid Network

PSC-FPN: Pixel Shuffling Algorithm for Channel Conversion in...

引用

International conference on Vision, image and signal processing (ICVISP)

作者： Quan Zeng Yungang Zhang School of Information Science and Technology Yunnan Normal University Kunming China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Object detection in remote sensing image is vital in areas like agriculture, urban development, and disaster evaluation. However, these images present substantial challenges because of their directional arbitrariness, large-scale variations, and dense object arrangements. While traditional Feature Pyramid Networks (FPN) have shown progress in multi-scale feature fusion, they suffer from issues such as information loss and aliasing effects. To address these limitations, this paper proposes an improved PSC-FPN network with enhanced information fusion. First, the Pixel Shuffle algorithm and sub-pixel convolution are introduced to design the PSCM module, effectively reducing information loss during channel conversion of high-level feature maps. Second, sub-pixel convolution replaces traditional linear interpolation in the top-down path, enhancing the quality of upsampled feature fusion. Additionally, a bottom-up path inspired by PAFPN strengthens the interaction between features at different scales. Finally, a $\mathbfimage\times \mathbfimage$ convolution replaces max pooling to generate additional feature levels, preserving more detailed information. Experimental results on the DOTA-v1.0 and DOTA-v1.5 datasets demonstrate that the improved PSC-FPN achieves significant performance gains, with mAP improvements of 1.42 % and 0.71%, respectively, compared to the baseline FPN model.

关键词： Interpolation Convolution Disasters signal processing algorithms Object detection Performance gain Feature extraction Agriculture remote sensing

来源：评论

学校读者我要写书评

暂无评论

Tensor-Based Chaotic Convolutional Neural Network for remote sensing Data Classification

Tensor-Based Chaotic Convolutional Neural Network for Remote...

引用

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

作者： Luobing Chen Junjun Yin Jian Yang University of Science and Technology Beijing Beijing China Tsinghua University Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

With the advancement of deep learning techniques, the classification of remote sensing data using artificial neural networks has emerged as a prominent research area. Despite this progress, the emulation of brain structures by traditional artificial neural networks remains at a relatively low level. Therefore, there is an urgent need to explore novel methods that can more effectively simulate human brain functions and enhance algorithmic performance. At the same time, artificial neural networks in image processing require the transformation of raw data into vector form. However, this conversion disrupts the inherent relationships between adjacent positional data in the raw data, resulting in the loss of crucial spatial structural information. To address these issues, we propose a tensor-based chaotic convolutional neural network model for the classification of remote sensing data. Firstly, we constructed a 3D discrete chaotic system, utilizing both Logistic mapping and Tent mapping, to achieve a more uniform iterative distribution and a broader full mapping range. This system was then integrated with convolutional neural networks to devise a novel chaotic convolutional neural network algorithm. By introducing chaotic mechanisms, this algorithm addresses the drawback of neural networks being prone to local minima. Secondly, we established a tensor model for remote sensing data. In contrast to existing methods that convert raw data into vector form, our approach represents raw data in tensor form, thereby preserving the spatial structural information among the three channels of the raw data. Finally, the effectiveness of the algorithm was validated using the NWPU-RESISC45 remote sensing dataset

关键词： Deep learning Tensors Three-dimensional displays Data models Vectors Classification algorithms Sensors Convolutional neural networks remote sensing Biological neural networks

来源：评论

学校读者我要写书评

暂无评论

Learning Deep Frequency Degradation Prior for remote sensing Spatio-temporal Fusion

Learning Deep Frequency Degradation Prior for Remote Sensing...

引用

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

作者： Yiting Bian Shenglong Hu Huihui Song Kaihua Zhang Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Existing deep learning-based remote sensing spatiotemporal fusion (STF) relies on a data-driven paradigm without considering the degradation prior modeling from the coarseto fine-resolution images. This makes the learned model easy to overfit to the training dataset, resulting in poor domain generalization across different datasets. To this end, this paper presents a deep frequency degradation prior to STF, dubbed as DeepFDP. The DeepFDP is based on a statistical observation that the frequency feature distributions of the tokens from the coarse- and fine-resolution images in different datasets have a low intra-resolution variance and a high inter-resolution variance with compact well-separated clusters. Therefore, the DeepFDP first designs a frequency proximity module to learn the mapping function between the token frequency representations of the coarse- and fine-resolution images, which is to narrow their feature distribution difference. Since the statistical properties of the token frequency representations are independent of the land-cover classes, the DeepFDP can be learned on a training set of limited image pairs without extra-supervision signals, which has a favorable zero-shot generalization capability across different datasets. Then, to faithfully recover the land-surface details, a high-frequency feature modulation module is designed that uses the fine-resolution image as guidance to progressively learn the multi-scale residual features in a coarse-to-fine fashion, yielding the fused features with rich high-frequency details. Finally, the progressively-fused features at each stage are fed into a hybrid fusion module, yielding the fine-resolution image prediction. Extensive evaluations on LGC and CIA datasets demonstrate favorable performance of the DeepFDP over state-of-the-art methods. Especially, the DeepFDP also shows a good zero-shot generalization performance when training on LGC and testing on CIA.

关键词： Degradation Training Deep learning Frequency modulation Acoustics Spatiotemporal phenomena Sensors Speech processing remote sensing Testing

来源：评论

学校读者我要写书评

暂无评论

Adaptive Spatial Modeling and Multi-Scale Attention Aggregation for Semantic Segmentation of remote sensing images

Adaptive Spatial Modeling and Multi-Scale Attention Aggregat...

引用

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

作者： Wenying Yang Yuchuan Zhang Yuxuan Jin Yupei Wang Liang Chen National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Chongqing Innovation Center Chongqing China School of Artificial Intelligence Jianghan University Wuhan China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Chongqing Innovation Center Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

As remote sensing technology advances and high-resolution sensors are deployed, the analysis of high-resolution remote sensing images encounters challenging issues, such as intra-class variability, and inter-class similarity, due to the unobvious appearance and complicated background. The high-resolution remote sensing images pose critical issues for precise semantic segmentation due to these fundamental issues. To this end, we propose an adaptive hierarchical aggregation network. Different from the previous approach of modeling semantic interdependencies solely within a single feature map in the channel dimension, we introduce multi-scale channel-wise feature aggregation to capture global contextual dependencies. Meanwhile, we design an adaptive detail-aware module to model long-range spatial dependencies, adaptively extracting effective details from different levels as compensation. Additionally, this module utilizes depth-wise separable convolution to selectively filter out irrelevant spatial details from lower-level feature maps. The outputs of the two modules are fused layer by layer to achieve refined feature representation, thereby generating precise pixel-level segmentation results. Our experiments are conducted on the Vaihingen dataset and the Potsdam dataset. The experimental results demonstrate that the proposed algorithm surpasses several existing state-of-the-art approaches.

关键词： image sensors Adaptation models Adaptive systems Semantic segmentation Semantics Filtering algorithms Feature extraction Data processing Sensors remote sensing

来源：评论

学校读者我要写书评

暂无评论

MAS-NET:Mixed-Feature Attention Siamese Network for Change Detection on remote sensing images

MAS-NET:Mixed-Feature Attention Siamese Network for Change D...

引用

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

作者： Xingyu Ding Weiqiang Wang CAS University of Chinese Academy of Sciences Beijing China

Change detection plays a crucial role in remote sensing tasks. However, current deep learning-based change detection methods suffer from issues such as misclassified pixels and unclear segmentation result on edges. To address these challenges, we propose a novel approach called Mixed-feature Attention Siamese Network (MAS-Net). MAS-Net adopts an encoder-decoder structure, where the encoder part effectively fuses image features from different time points, thereby preserving more cross-time information in the feature map. In the decoder part, we introduce the Feature Global Attention Module (FGAM) to leverage the global attention mechanism for extracting deep semantic information from the fused feature map. By incorporating these proposed modules and strategies, MAS-Net achieves fewer misclassified pixels and clearer edges in the resulting change detection maps. Experimental evaluations on the LEVIR-CD and CDD datasets demonstrate that MAS-Net outperforms state-of-the-art models by 0.15% (91.88% vs. 91.73%) and 1.3% (97.5% vs. 96.2%) in terms of F1-Score, respectively, thus establishing a solid baseline for change detection.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Method of Efficient Synthesizing Post-disaster remote sensing image with Diffusion Model and LLM

A Method of Efficient Synthesizing Post-disaster Remote Sens...

引用

Asia-Pacific signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Ruizhe Ou Haotian Yan Ming Wu Chuang Zhang Beijing University of Posts and Telecommunications China

Due to the fact that current deep learning models are typically driven by big data, existing interpretation models for emergency management lack relevant learning data. However, existing pre-trained image generative models cannot directly generate post-disaster remote sensing images without fine-tuning. In this paper, we demonstrate the ability of natural language guidance synthesizing remote sensing imagery affected by disaster by pre-trained image generative model fine-tuned with very few unlabelled images (i.e., less than 100 fine-tuning images) at very low training cost (i.e., one 2080Ti GPU). To trade for lower cost, we embrace the trend of large model, leveraging a pre-trained caption model, GPT-4 and a pre-trained text-to-image Stable Diffusion model for this task. The Stable Diffusion Model, fine-tuned with our method, successfully synthesizes remote sensing images affected by disasters using natural language guidance in both image inpainting and image generation tasks. In addition, the ground truth for other interpretation models learning. With this achievement, our method can synthesize a large amount of data for the emergency management interpretation model to learn when there is less existing data, only unlabelled data and less time, so as to achieve better interpretation performance. Furthermore, our approach highlights the significant of combining human feedback with large models in synthesizing data which is out of the prior knowledge of large model, especially when there is less data available and less computational power available.

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362页 << < 48 49 50 51 52 53 54 55 56 57 > >>

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：