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

3,540 篇 会议
35 篇 期刊文献
8 册 图书

馆藏范围

3,583 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,556 篇 工学
- 1,043 篇 电气工程
- 993 篇 计算机科学与技术...
- 902 篇 测绘科学与技术
- 711 篇 信息与通信工程
- 526 篇 软件工程
- 217 篇 光学工程
- 190 篇 仪器科学与技术
- 155 篇 电子科学与技术（可...
- 155 篇 控制科学与工程
- 155 篇 航空宇航科学与技...
- 90 篇 生物工程
- 82 篇 机械工程
- 62 篇 化学工程与技术
- 61 篇 生物医学工程（可授...
- 49 篇 建筑学
- 45 篇 土木工程
- 44 篇 环境科学与工程（可...
1,433 篇 理学
- 1,022 篇 物理学
- 244 篇 数学
- 101 篇 生物学
- 93 篇 系统科学
- 86 篇 化学
- 83 篇 统计学（可授理学、...
- 43 篇 大气科学
- 29 篇 地质学
- 28 篇 地球物理学
679 篇 医学
- 677 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...
39 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

1,083 篇 remote sensing
202 篇 image processing
174 篇 feature extracti...
169 篇 image segmentati...
168 篇 synthetic apertu...
154 篇 satellites
137 篇 signal processin...
119 篇 image resolution
111 篇 hyperspectral im...
110 篇 image reconstruc...
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
70 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...
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 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 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

3,524 篇 英文
32 篇 土耳其文
18 篇 其他
7 篇 中文
2 篇 俄文

检索条件"任意字段=Conference on Image and Signal Processing for Remote Sensing XXIV"

共 3583 条记录，以下是21-30 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：

MMCD: Memory-Based Multimodal Change Detection

MMCD: Memory-Based Multimodal Change Detection

引用

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

作者： Zhang, Limeng Zhang, Zenghui Wu, Juanping Guo, Weiwei Zhang, Tao Yu, Wenxian Shanghai Jiao Tong University Shanghai200240 China Center of Digital Innovation Tongji University Shanghai200092 China

ISBN: (纸本)9798350368741

Single-modal change detection methods based on optical or Synthetic Aperture Radar (SAR) images face challenges such as degradation due to adverse weather or noise interference. In contrast, multimodal change detection struggles with significant domain gaps between different modalities. Inspired by the SAM2 model's temporal memory mechanism for video segmentation, this paper introduces the concept of memory into change detection and proposes a novel approach called Memory-based Multimodal Change Detection (MMCD). By treating change detection as a temporal problem and modeling remote sensing images as video sequences, the proposed method integrates historical optical images with current SAR images to enhance detection accuracy. Additionally, a difference map enhancement module is introduced to mitigate false changes caused by modality discrepancies. Experimental results show that this approach achieves state-of-the-art performance in multimodal change detection, demonstrating the effectiveness of the proposed method. © 2025 IEEE.

关键词： difference enhancement Memory attention multimodal change detection optical image assistance

来源：评论

学校读者我要写书评

暂无评论

remoteTrimmer: Adaptive Structural Pruning for remote sensing image Classification

RemoteTrimmer: Adaptive Structural Pruning for Remote Sensin...

引用

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

作者： Guangwenjie Zou Liang Yao Fan Liu Chuanyi Zhang Xin Li Ning Chen Shengxiang Xu Jun Zhou Hohai University Nanjing China Griffith University Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Since high resolution remote sensing image classifi-cation often requires a relatively high computation complexity, lightweight models tend to be practical and efficient. Model pruning is an effective method for model compression. However, existing methods rarely take into account the specificity of remote sensing images, resulting in significant accuracy loss after pruning. To this end, we propose an effective structural pruning approach for remote sensing image classification. Specifically, a pruning strategy that amplifies the differences in channel importance of the model is introduced. Then an adaptive mining loss function is designed for the fine-tuning process of the pruned model. Finally, we conducted experiments on two remote sensing classification datasets. The experimental results demonstrate that our method achieves minimal accuracy loss after compressing remote sensing classification models, achieving state-of-the-art (SoTA) performance.

关键词： Training Adaptation models Accuracy image coding Computational modeling signal processing algorithms signal processing Speech processing remote sensing image classification

来源：评论

学校读者我要写书评

暂无评论

DSDN-Net: An Effective Network for Semantic Segmentation in Open-Pit Coal Mining Areas for Land Cover Recognition

DSDN-Net: An Effective Network for Semantic Segmentation in ...

引用

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

作者： Jiaqi Li Ming Ma College of Computer Science-College of Software Inner Mongolia University Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Currently, existing methods in land cover recognition in open-pit coal mining areas face the issue of insufficient accuracy due to multiscale and blurred boundaries when processing remote sensing images. This paper introduces a remote sensing image semantic segmentation network, DSDN-Net, to tackle the issues. DSDN-Net adopts MobileNetV2 as the backbone, and a spatial pyramid pooling structure, Dynamic Snake Dense-ASPP (DSDN-ASPP), which enhances the model’s receptive field based on cross-layer connections is designed to increase the model’s receptive field by introducing dynamic snake convolution and utilizing depthwise convolutions with different kernel sizes, allowing the model to focus more on spatial features in remote sensing images. To handle blurred boundaries in remote sensing image, the decoder of DSDN-Net incorporates a Convolutional Block Attention Module (CBAM) to enhance precision. A dataset containing 5440 remote sensing images from the open-pit coal mining area is constructed using high-resolution remote sensing image. Experimental results on the open-pit coal mining area dataset demonstrate that the proposed DSDN-Net outperforms existing methods in multiple performance metrics.

关键词： image recognition Accuracy Convolution Target recognition Semantic segmentation Land surface signal processing algorithms Feature extraction Coal mining remote sensing

来源：评论

学校读者我要写书评

暂无评论

DiffRS: An Extensible Diffusion Model for remote sensing image Generation

DiffRS: An Extensible Diffusion Model for Remote Sensing Ima...

引用

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

作者： Xinyue Huang Xin Niu Jingfei Jiang Hengyue Pan National University of Defense and Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

remote sensing image generation is of great value for virtual environment creation and adversarial learning for fake news detection. It could also address the learning sample shortage in the region of interest. However, most current image generation methods are limited to producing images of fixed sizes, few studies on extensible natural image generation largely focus on the stitching of random contents, lacking effective exploration of contextual information, which weakens the coherence of the extended images. To address this problem, we propose an extensible generation method for remote sensing images with the model DiffRS. This approach allows for sequential extension of arbitrary sizes by exploring the generated neighboring regions. The method is particularly suitable for scenes like remote sensing images where a generation block could cover multiple independent targets, rather than natural image tasks which may stitch across regions to form a completely target. Compared to the state-of-the-art extensible generation methods, DiffRS could improve the large scale image generation with better structure consistency, richer details and higher realism. Experiments showed that DiffRS could improve the FID score by 4.6% and 3.2% respectively in comparison with the MultiDiffusion and Mixture of Diffuser models.

关键词： image synthesis Virtual environments Coherence signal processing Diffusion models Adversarial machine learning Sensors Speech processing Fake news remote sensing

来源：评论

学校读者我要写书评

暂无评论

Bridging Task Boundaries: remote sensing image-Text Retrieval via Dictionary-Driven Adaptation

Bridging Task Boundaries: Remote Sensing Image-Text Retrieva...

引用

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

作者： Junwei Xu Tao Huang Zhenyu Wang Weisheng Dong Xin Li Hangzhou Institute of Technology Xidian University Hangzhou China the School of Artificial Intelligence Xidian University Xi’an China Department of Computer Science State University of New York at Albany New York USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Given image (or text), remote sensing image-text retrieval (RSITR) aims to retrieve corresponding text (or image) within diverse remote sensing data. However, due to the complex scenes and compact distribution of targets in remote sensing data, existing methods, particularly those leveraging large models like CLIP, often generate features with high intra-modal similarity and insufficient distinctive characteristics, thus resulting in suboptimal retrieval performance. To address these issues, we pro- pose a novel dictionary-based RSITR method that jointly models image and text feature estimation. Specifically, by incorporating a general dictionary and the corresponding sparse coefficients, our method more effectively captures the correlations between the learned features. Furthermore, we introduce adaptive weighted metric learning on a sample-by-sample basis to encourage the model to focus on more challenging negative samples, promoting fine-grained feature alignment. The extensive experiments on the RSICD and RSITMD datasets demonstrate the effectiveness of our method, demonstrating significant improvements in retrieval performance.

关键词： Measurement Adaptation models Dictionaries Semantics Estimation signal processing Feature extraction Data models Speech processing remote sensing

来源：评论

学校读者我要写书评

暂无评论

SlotFusion: Object-Centric Audiovisual Feature Fusion with Slot Attention for remote sensing Scene Recognition

SlotFusion: Object-Centric Audiovisual Feature Fusion with S...

引用

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

作者： Fangzhou Han Tianyi Yu Lamei Zhang Lingyu Si Yiqi Zhang Dept. of Information Engineering Harbin Institute of Technology Harbin China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Despite significant advancements in remote sensing multimodal learning, particularly in image-image feature fusion, the exploration of audio-image feature fusion remains insufficient. Given the complexity and redundancy of ground objects in remote sensing images, accurately aligning audio features with image features during the fusion process is a critical challenge. In this paper, we introduce an object-centric feature fusion method named SlotFusion. By employing a slot attention-based feature decoupling module and a slot-based audiovisual feature fusion module, we transform modality features with complex semantic information into a set of slot features corresponding to object units and use gated activation units to adaptively implement object-centric feature fusion. Experiments on the Audio Visual Aerial Scene Recognition dataset (ADVANCE) demonstrate that the proposed SlotFusion significantly improves remote sensing scene recognition performance, with a 7.04% increase in overall accuracy compared to previous methods, achieving state-of-the-art results.

关键词： Visualization Semantics Termination of employment Transforms Interference signal processing Logic gates Robustness Speech processing remote sensing

来源：评论

学校读者我要写书评

暂无评论

HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

引用

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

作者： Zhang, Pei Wang, Dong Wu, Chanyue Yang, Jing Kang, Lei Bai, Zongwen Li, Ying Shen, Qiang School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an University Yan'an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd. Qingdao China Department of Computer Science Aberystwyth University Aberystwyth United Kingdom

ISBN: (纸本)9798350368741

Hyperspectral image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques. © 2025 IEEE.

关键词： Cross-scene classification Diffusion model Hyperspectral image Transformer

来源：评论

学校读者我要写书评

暂无评论

AMIC: A Novel Hybrid image Compression for Multi-spectral Satellite imagery 3rd

AMIC: A Novel Hybrid Image Compression for Multi-spectral S...

引用

3rd International conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Nhat, Trinh Le Nhat, Tan Thai Nguyen, Luong Vuong Vu, Bui Cao Department of Artificial Intelligence FPT University Danang Viet Nam

ISBN: (纸本)9789819624676

Multi-spectral satellite imagery (MSSI) offers a crucial means of collecting environmental and geological data from dangerous, remote locations. However, the substantial volume of such imagery complicates its storage and transmission, making efficient image compression indispensable. Traditional compression methods such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), Consultative Committee for Space Data Systems (CCSDS), Bandelet, and JPEG 2000 have provided high reliability, simple implementation, and effectiveness, yet they lack sufficient adaptability. In contrast, Machine Learning (ML) offers a more adaptable, albeit complex and data-intensive, alternative. This paper proposed a hybrid approach, called AMIC, that combines the robustness of traditional methods with the flexibility of ML, using DWT to quantize image bands and ML to refine this process, thereby enhancing the compression quality with minimal computational load. The AMIC performance demonstrates high fidelity in compressed images, with Peak signal-to-Noise Ratio (PSNR) values ranging from 30dB to 42dB and Structural Similarity Index (SSIM) values above 0.78, ensuring excellent preservation of image details. The approach significantly optimizes compression and decompression times, achieving a reduction to 43.26% of the original size. This hybrid method ensures high-quality satellite image compression for improved transmission and storage. It broadly impacts real-time data processing across various fields, including environmental monitoring and remote sensing research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Satellite imagery

来源：评论

学校读者我要写书评

暂无评论

Hazy remote sensing image Semantic Segmentation with Weak Annotations via Pre-training Optimization and Co-training

Hazy Remote Sensing Image Semantic Segmentation with Weak An...

引用

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

作者： Junda Xu Libao Zhang School of Artificial Intelligence Beijing Normal University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, weakly supervised semantic segmentation has emerged as a prominent research topic in the field of remote sensing image semantic segmentation due to its cost-effective labeling advantages. However, the presence of haze in remote sensing images poses significant challenges to accurate semantic segmentation. Despite the numerous haze removal methods developed for remote sensing images, their efficacy in the subsequent task of semantic segmentation remains inadequate. To address these issues, this paper aims to enhance the robustness of the segmentation network against haze interference by proposing a weakly supervised semantic segmentation framework based on pre-training optimization and dual-network co-training. The proposed approach employs a pseudo-label optimization network to effectively filter out noise interference and subsequently trains two parallel segmentation networks that mutually guide each other for enhanced robustness. Additionally, an edge optimization loss is introduced to improve prediction accuracy by incorporating both edge and texture information. Experimental comparisons with alternative methods across multiple datasets validate the superior performance of our proposed method.

关键词： Accuracy Semantic segmentation image edge detection Noise Interference Robustness Sensors Speech processing Optimization remote sensing

来源：评论

学校读者我要写书评

暂无评论

A Cost-effective Solution for remote sensing image Segmentation via Train/Test-Time Adaptation

A Cost-effective Solution for Remote Sensing Image Segmentat...

引用

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

作者： Wei Chen Xin Luo Yulin He Tianrui Liu Di Wu Tianhang Guo Yuhang Li Yuhua Tang National University of Defense Technology Changsha China National Innovation Institute of Defense Technology Academy of Military Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

remote sensing image (RSI) segmentation has made significant strides, emerging as a leading solution for interpreting remote sensing data. However, due to the substantial domain gap between different remote sensors and limited computational resources, existing RSI segmentation methods often suffer from poor generalization performance. To address these challenges, we propose a COst-effective and Whole-process Domain Adaptation solution, namely COWDA, which adapts models at both the train and test time through three key phases: 1) Source-data Domain Alignment: We employ traditional image stylization techniques to translate source images into target-style alternatives, which avoids the computationally intensive need for auxiliary neural network models. 2) Target-data Train-time Fine-tuning: We propose a joint positive and negative learning (JPNL) algorithm that adds both positive and negative samples to effectively learn domain-invariant knowledge from noisy pseudo-labeled target data. 3) Test-time Adaptation: We propose an entropy-weighted test-time adaptation strategy to update the trained model with online test samples, further enhancing its performance. Extensive experiments on two widely-used domain adaptation benchmarks for remote sensing show that COWDA improves state-of-the-art counterparts by 1.4% and 2.6% F1 scores, respectively.

关键词： Adaptation models image segmentation Computational modeling signal processing algorithms Benchmark testing Data models Noise measurement Speech processing remote sensing signal resolution

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 << < 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：