检索结果-内蒙古大学图书馆

IEEE International Geoscience and Remote Sensing Symposium

作者： Jiayin Liu Xiaolan Qiu Baoquan Zhang Feng Wang Lei Li Key Laboratory of Technology on Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Institute of Electronics Chinese Academy of Sciences Beijing China

The GF-3 satellite is the first full-polarization SAR satellite in China, which operates in the C band with a resolution of 1m. This paper presents a dual-aspect geometric terrain correction method to overcome the inherent shortages of SAR image such as foreshortening, shadow and layover. The results using GF-3 satellite data show that the method can effectively eliminate layover and shadow distortions in SAR images. This method solves the geometric correction problem that cannot be solved with a single SAR image.

关键词： Satellites Radar polarimetry Synthetic aperture radar Mathematical model Orbits Image resolution Satellite broadcasting

来源：评论

学校读者我要写书评

暂无评论

Nonparametric statistics inspired similarity measure for accuracy in collaborative filtering

引用

Journal of Computational information systems 2013年第20期9卷 8397-8406页

作者： Liu, Shichen Xiong, Yan Liu, Qingwen Qi, Xiang Department of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing 100190 China

Similarity computation is especially significant in collaborative filtering algorithms. In the existed literatures or large recommender systems, researchers generally use cosine similarity or Pearson correlation coefficient to compute the similarities. However, on one hand, both two measures are more applicable in linear space while the ratings cannot linearly reflect users' interest intensity. On the other hand, different systems have different rating restricts, so it's unreasonable to treat varied systems with the same computation method. In this work, inspired by nonparametric statistics, a sound memory-based method with a novel similarity measure is proposed to eliminate the affection of rating distributions and system diversities on similarity computation. Experiments based on Jester are set to evaluate the performance of the approach. And the highly competitive results show that our approach is able to significantly improve the recommendation quality. © 2013 Binary information Press.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

A DESIGN OF THE COMPLETE POLARIZATION CONVERTER USING DIELECTRIC PERIODIC STRUCTURES

A DESIGN OF THE COMPLETE POLARIZATION CONVERTER USING DIELEC...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Yueting Zhang Chibiao Ding Bin Lei Weihai Fang Key Laboratory of Geo-spatial Information Processing and Application System Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing General Institute of Electronic Engineering

Polarization converter is used in the applications of the polar SAR observations. There exists coupling between TE and TM modes when plane wave is oblique incident on the surface of dielectric periodic structure, the single TE or TM polarized wave incident will cause TE and TM mixed transmission wave. In some proper incident conditions, complete polarization conversion can be realized between TE and TM mode. In this work, a design of complete polarization converter by using dielectric periodic structure is designed and it is carefully investigated by a method which combines the multimode network theory with the rigorous mode matching method. We revealed TE/TM complete polarization conversion characteristics of dielectric periodic structure, and also analyzed the effects of structure parameters. These investigations provide important guideline for accurate designing new millimeter wave polarization converters.

关键词： Dielectrics Polarization Periodic structures Optical waveguides Microwave filters Reflection

来源：评论

学校读者我要写书评

暂无评论

1-km/Daily Land Surface Temperature Optimized Dataset for the Qinghai-Tibet Plateau Based on MODIS Data(2000-2020)

全球变化数据仓储（中英文）

引用

全球变化数据仓储（中英文） 2023年第10期 4-7页

作者： XU Xunpeng ZHANG Yu JI Luyan TANG Hairong Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China the Key Laboratory of Technology in Geo-Spatial information Processing and Application System Chinese Academy of SciencesBeijing 100190China the School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 101408China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China the Key Laboratory of Technology in Geo-Spatial information Processing and Application System Chinese Academy of SciencesBeijing 100190China

Remote sensing data has strong correlation and continuity in space and time,so time series remote sensing images have low-rank *** this dataset,we repaired images using low-rank tensor ***,we preprocessed the MODIS land surface temperature data and employed spatio-temporal interpolation to initially fill in the missing values caused by cloud ***,we treated the land surface temperature time series data as a third-order spatio-temporal tensor and introduced Fourier transform on the time dimension to convert it into a space-frequency *** performing singular value decomposition and Gaussian low-pass filtering on this tensor,followed by inverse Fourier transform,we obtained a space-time ***,we further optimized the missing tensor using the alternating direction method of *** data accuracy using the method was validated through simulation experiments,where artificial masks were added and subsequently *** resulting mean absolute error(MAE)falls within the range of 2.1℃to 4.9℃.This dataset includes the following data for the Tibetan Plateau on a daily basis for the years 2000-2020:(1)the optimized surface temperature data for the cloud-shaded regions of the MOD11A1,MYD11A1 products(MOD11A1_QTP_PART,MYD11A1_QTP_PART);(2)optimized MOD11A1/MYD11A1 data(MOD11A1_QTP_TEMP,MYD11A1_QTP_TEMP);and(3)original MOD11A1 and MYD11A1 products(MOD11A1_QTP_ORIGIN,MOD11A1_QTP_ORIGIN).All data have a spatial resolution of 1 km and are stored in an integer data format,with pixel value representing the thermodynamic temperature of the surface with a scale factor of 0.02 in *** dataset is archived *** format,and consists of 43833 data files with data size of 143 GB(compressed into 21 files with 138 GB).

关键词：

来源：评论

学校读者我要写书评

暂无评论

DEM reconstruction of mountainous area from two anti-parallel aspects of airborne InSAR data

DEM reconstruction of mountainous area from two anti-paralle...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Fangfang Li Donghui Hu Jiayin Liu Xiaolan Qiu Chibiao Ding Institute of Electronics Chinese Academy of Sciences Beijing China Key Laboratory of Geo-Spatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing China

The geometry distortion phenomenon of SAR imaging arises in the mountainous scenarios due to the presence of strong terrain slopes. Because of phase discontinuities or the absence of valid phase, for single pass interferometric SAR (InSAR), it is difficult to recover accurate digital elevation model (DEM) in such areas. Fusion of two or more different aspects of InSAR data is practicable to deal with this problem. In this paper, the processing procedures of airborne InSAR data are presented. In order to decrease the processing error of every single aspect data, an iterative motion compensation (MOCO) method is used. Besides, the interferometric phase of shadow area is linearly complemented before phase unwrapping to avoid error spreading. Experimental results using two anti-parallel aspects of airborne InSAR data validate the feasibility of fusion.

关键词： Geometry Motion compensation Iterative methods Surfaces Aerospace electronics Radar polarimetry

来源：评论

学校读者我要写书评

暂无评论

Large Language Models are Good Attackers: Efficient and Stealthy Textual Backdoor Attacks

arXiv

引用

arXiv 2024年

作者： Li, Ziqiang Zeng, Yueqi Xia, Pengfei Liu, Lei Fu, Zhangjie Li, Bin Nanjing University of Information Science and Technology China Big Data and Decision Lab University of Science and Technology of China China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System China

With the burgeoning advancements in the field of natural language processing (NLP), the demand for training data has increased significantly. To save costs, it has become common for users and businesses to outsource the labor-intensive task of data collection to third-party entities. Unfortunately, recent research has unveiled the inherent risk associated with this practice, particularly in exposing NLP systems to potential backdoor attacks. Specifically, these attacks enable malicious control over the behavior of a trained model by poisoning a small portion of the training data. Unlike backdoor attacks in computer vision, textual backdoor attacks impose stringent requirements for attack stealthiness. However, existing attack methods meet significant trade-off between effectiveness and stealthiness, largely due to the high information entropy inherent in textual data. In this paper, we introduce the Efficient and Stealthy Textual backdoor attack method, EST-Bad, leveraging Large Language Models (LLMs). Our EST-Bad encompasses three core strategies: optimizing the inherent flaw of models as the trigger, stealthily injecting triggers with LLMs, and meticulously selecting the most impactful samples for backdoor injection. Through the integration of these techniques, EST-Bad demonstrates an efficient achievement of competitive attack performance while maintaining superior stealthiness compared to prior methods across various text classifier datasets. © 2024, CC BY.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

The Research on the Space-Time Variation of Phase Imbalance for GF-3 Azimuth Multichannel Mode

The Research on the Space-Time Variation of Phase Imbalance ...

引用

Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Mingyang Shang Xiaolan Qiu Bing Han Lihua Zhong Chibiao Ding Junying Yang Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728129129

ISBN: (纸本)9781728129136

Azimuth multichannel (AMC) synthetic aperture radar (SAR) is an advanced technique which can prevent the minimum antenna area constraint and provide high-resolution and wide-swath (HRWS) SAR images. Channel imbalance calibration is an important topic in AMC SAR signal processing, since it has a great impact on the image quality. The phase imbalance of AMC SAR is not only related to the radar system, but also to the satellite attitude. This paper first models the relationship between phase imbalance and satellite attitude, and analyzes the influence of satellite attitude on the phase imbalance. In the presence of the satellite attitude deviation, the phase imbalance is time-variation in azimuth, and spatial-variation in range. Then, the real data acquired by Gaofen-3 (GF-3) in the ultra-fine strip-map (UFS) mode is used to verify the relationship model both in theoretical calculations and estimation. The result shows that the model proposed in this paper can describe the space-time variation of phase imbalance very accurately. Finally, some discussions and conclusions are drawn and the future research perspectives are proposed.

关键词：

来源：评论

学校读者我要写书评

暂无评论

TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded ...

引用

2022 International Conference on Radar systems, RADAR 2022

作者： Wang, Muhan Zhang, Zhe Wang, Yue Gao, Silin Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States

ISBN: (纸本)9781839537776

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Compressed sensing

来源：评论

学校读者我要写书评

暂无评论

Temporal Context Mining for Learned Video Compression

arXiv

引用

arXiv 2021年

作者： Sheng, Xihua Li, Jiahao Li, Bin Li, Li Liu, Dong Lu, Yan The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China Microsoft Research Asia Beijing100080 China

Applying deep learning to video compression has attracted increasing attention in recent few years. In this work, we address end-to-end learned video compression with a special focus on better learning and utilizing temporal contexts. We propose to propagate not only the last reconstructed frame but also the feature before obtaining the reconstructed frame for temporal context mining. From the propagated feature, we learn multi-scale temporal contexts and re-fill the learned temporal contexts into the modules of our compression scheme, including the contextual encoder-decoder, the frame generator, and the temporal context encoder. We discard the parallelizationunfriendly auto-regressive entropy model to pursue a more practical encoding and decoding time. Experimental results show that our proposed scheme achieves a higher compression ratio than the existing learned video codecs. Our scheme also outperforms x264 and x265 (representing industrial software for H.264 and H.265, respectively) as well as the official reference software for H.264, H.265, and H.266 (JM, HM, and VTM, respectively). Specifically, when intra period is 32 and oriented to PSNR, our scheme outperforms H.265-HM by 14.4% bit rate saving;when oriented to MS-SSIM, our scheme outperforms H.266-VTM by 21.1% bit rate saving. Copyright © 2021, The Authors. All rights reserved.

关键词： Image compression

来源：评论

学校读者我要写书评

暂无评论

SAR Moving Target Segmentation and Removal Based on Deep Learning 8th

SAR Moving Target Segmentation and Removal Based on Deep L...

引用

8th EAI International Conference on Machine Learning and Intelligent Communications, MLICOM 2023

作者： Wu, Yifan Qi, Xiyu Huang, Lijia Zhang, Bingchen Yan, Lili Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China China Centre for Resources Satellite Data and Application Beijing100094 China

ISBN: (纸本)9783031717154

Synthetic Aperture Radar (SAR) stands as an integral part of advanced remote sensing technology. Nevertheless, practical applications experience inevitable disturbances from moving target noise, compromising both image integrity and target detection performance. This paper introduces a pioneering approach reliant on deep learning principles for the elimination of moving target noise within SAR imaging. Firstly, we use the Back-Projection (BP) algorithm to form the foundational images from echo signals. Employing the Unet network, we subsequently acquire a segmentation map of the moving target noise. By subtracting this segmented noise map from the original image, we succeed in the effective erasure of moving targets, yielding a resultant image devoid of moving target noise. Experimental validation demonstrates that our method can effectively remove moving target noise and yield SAR images that solely contain static scenes. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：