检索结果-内蒙古大学图书馆

IEEE International geoscience and Remote Sensing Symposium

作者： Niangang Jiao Feng wang Hongjian You Kun Hu Mudan Yang Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Institute of Electronics Beijing China

geo-positioning accuracy improvement is one of the most important step of remote sensing image preprocessing. Traditional methods require a large number of ground control points (GCPs) which consuming lots of manpower and financial resources. With the resolution up to 0.8m, the original geo-positioning accuracy of the Chinese Gaofen (GF-2) multi-angle imagery is about 90m which means a limited application in geometric processing. In this paper, we propose a new method to improve the geometric performance of the multi-angle satellite imagery based on the geometric error sources of this experimental dataset without GCPs. Under the condition of weak intersection of our test dataset, we use a DEM-assisted approach to acquire a more accurate initial position accuracy of all tie points, and all extracted data is clustered by the Density based spatial clustering of applications with noise (DBSCAN) algorithm in order to eliminate points or impages with large positioning error automatically. Then, the error-based block adjustment model are proposed and investigated to improved the geometric performance of the experimental dataset. Based on our proposed method, 142 multi-angle GF-2 satellite images covering the western Beijing area are experimented and the root mean square error (RMSE) of the geometric accuracy is improved up to about 12m in plane and 6m in height, which shows a significantly improvement in geo-positioning accuracy of these multi-angle GF-2 remote sensing imagery.

关键词： Satellites Image resolution Mathematical model Unified modeling language Remote sensing Clustering algorithms Cameras

来源：评论

学校读者我要写书评

暂无评论

A Simple Topographic Correction Method Based on Smoothed Terrain

A Simple Topographic Correction Method Based on Smoothed Ter...

引用

International Symposium on Image and Data Fusion (ISIDF)

作者： Kang Jiang Yong-chao Zhao Xiu-rui Geng Key Laboratory of Technology in Geo-spatial information Processing and Application System Institute of Electronics Chinese Academy of Sciences (IECAS) Beijing China

A simple topographic correction method based on smoothed terrain is proposed in this paper. The main idea of the new method is smoothing the slope angle of terrain in the first place and then performing the cosine correction based on the smoothed terrain. The smoothing effect on the terrain is significant in forested mountainous areas and a model is proposed to explain the effect physically. A comparison is conducted among the new method and several other common methods like C, SCS, Minnaert corrections using Landsat-7 ETM+ data in Shanxi Province in China. Visual analysis and statistical analysis are adopted to assess the performance of these methods from two aspects: overcorrection, homogeneity within a land cover class. Comparison results indicate that the smoothing step can significantly reduce the overcorrection and the performance of the new method is on the same level with C correction. The new method does not require any training data from image to obtain parameters and proves to be an effective and simple method in forested areas.

关键词： Remote sensing Smoothing methods Satellites Visualization Surfaces Optical sensors Adaptive optics

来源：评论

学校读者我要写书评

暂无评论

Online filter clustering and pruning for efficient convnets

arXiv

引用

arXiv 2019年

作者： Zhou, Zhengguang Zhou, Wengang Hong, Richang Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System EEIS Department University of Science and Technology of China HeFei University of Technology

Pruning filters is an effective method for accelerating deep neural networks (DNNs), but most existing approaches prune filters on a pre-trained network directly which limits in acceleration. Although each filter has its own effect in DNNs, but if two filters are same with each other, we could prune one safely. In this paper, we add an extra cluster loss term in the loss function which can force filters in each cluster to be similar online. After training, we keep one filter in each cluster and prune others and fine-tune the pruned network to compensate the loss. Particularly, the clusters in every layer can be defined firstly which is effective for pruning DNNs within residual blocks. Extensive experiments on CIFAR10 and CIFAR100 benchmarks demonstrate the competitive performance of our proposed filter pruning method. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

An Accurate Sparse SAR Imaging Method for Joint Feature Enhancement Based on Nonconvex-Nonlocal Total Variation Regularization 14

An Accurate Sparse SAR Imaging Method for Joint Feature Enha...

引用

14th European Conference on Synthetic Aperture Radar, EUSAR 2022

作者： Xua, Zhonqiu Zhoua, Guoru Zhanga, Bingchen Zhang, Zhe Wu, Yirong Key Laboratory of Spatial Information Processing and Application System Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783800758234

Synthetic aperture radar (SAR) imaging under the sparse constraint is a developing SAR imaging scheme that emerged in the recent decade. In sparse SAR imaging, the sparsity can be regarded as a priori information, used to enhance different types of imaging features, among which `1-norm and the total variation (TV)-norm are two widely used constraints in the reconstruction model corresponding to the features of point targets and distributed targets respectively. However, `1 regularization often generates a biased estimation and the TV regularization always over-smooths isolated point targets in the scene. In this paper, we propose the nonconvex-nonlocal total variation (NLTV) regularization, which can realize the joint feature enhancement of sparse SAR imaging. The modified variable splitting-Alternating direction method of multipliers (VS-ADMM) is introduced to solve the compound regularization. The performance of the proposed method is verified using simulated data and Gaofen-3 (GF-3) SAR data. © 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

IMPROVED CONJUGATE GRADIENT BUNDLE ADJUSTMENT of DUNHUANG WALL PAINTING IMAGES

IMPROVED CONJUGATE GRADIENT BUNDLE ADJUSTMENT of DUNHUANG WA...

引用

ISPRS geospatial Week 2017

作者： Hu, K. Huang, X. You, H. Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences No. 19 North 4th Ring Road West Beijing100190 China Wuhan Engineering Science and Technology Institute Jiangda Road 30 Wuhan Hubei430019 China

Bundle adjustment with additional parameters is identified as a critical step for precise orthoimage generation and 3D reconstruction of Dunhuang wall paintings. Due to the introduction of self-calibration parameters and quasi-planar constraints, the structure of coefficient matrix of the reduced normal equation is banded-bordered, making the solving process of bundle adjustment complex. In this paper, Conjugate Gradient Bundle Adjustment (CGBA) method is deduced by calculus of variations. A preconditioning method based on improved incomplete Cholesky factorization is adopt to reduce the condition number of coefficient matrix, as well as to accelerate the iteration rate of CGBA. Both theoretical analysis and experimental results comparison with conventional method indicate that, the proposed method can effectively conquer the ill-conditioned problem of normal equation and improve the calculation efficiency of bundle adjustment with additional parameters considerably, while maintaining the actual accuracy. © Authors 2017.

关键词： Calculus of variations Conjugate gradient bundle adjustment (CGBA) Dunhuang wall painting images Improved incomplete Cholesky factorization Space complexity Time complexity

来源：评论

学校读者我要写书评

暂无评论

引用

Journal of Computational information systems 2014年第17期10卷 7259-7267页

作者： Qi, Xiang Wu, Wei Huang, Yu Huang, Tinglei Fu, Kun Wang, Hongqi Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Institute of Electronics University of Chinese Academy of Sciences Beijing China University of Science and Technology of China Hefei China

In this paper we propose the rating Correlated Topic Model for rating-based collaborative filtering, which improves the performance of the state-of-the-art Latent Semantic Models in two aspects: (1), making the prediction accuracy more robust to the topic number K;(2), improving the recommendation quality for users with few existed ratings. We achieve our goals by employing the Logistic Normal distribution to capture the correlation between latent topics following the Correlated Topic Model, as well as modifying the generative process to meet the requirement of rating-based collaborative filtering. We derive a parameter estimation algorithm based on variational inference for the proposal. Experiment results on the Movielens data set demonstrate our model's advantages on both referred aspects.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

GRIDLESS DOA ESTIMATION FOR AUTOMOTIVE RADARS WITH VARIOUS ARRAY geoMETRIES: THE NON-VANDERMONDE ATOMIC SOFT THRESHOLDING APPROACH

GRIDLESS DOA ESTIMATION FOR AUTOMOTIVE RADARS WITH VARIOUS A...

引用

IET International Radar Conference 2023, IRC 2023

作者： Gao, Silin Wang, Muhan Zhang, Zhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China

ISBN: (纸本)9781839539954

This paper is centered on gridless direction of arrival (DoA) estimation for single-snapshot data collected by non-uniform linear arrays (NLAs) in the context of automotive applications. While recent single-snapshot DoA estimation algorithms rooted in grid-based compressed sensing (CS) offer super-resolution capabilities, they are notably sensitive to discrepancies between the assumed sparsity basis and the actual basis. Furthermore, the existing atomic soft thresholding (AST) algorithm, a promising gridless sparse recovery technique within the Toeplitz model, is constrained to uniform linear arrays (ULAs) exhibiting Vandermonde structure in their array manifolds. However, in automotive scenarios, it becomes imperative to efficiently apply gridless DoA estimation to NLAs featuring non-Vandermonde array manifolds. In this paper, we introduce the non-Vandermonde atomic soft thresholding (NVAST) algorithm and employ it for DoA estimation in NLAs. The novel algorithm establishes a link between Vandermonde and non-Vandermonde atoms by decomposing non-Vandermonde vectors and addressing them using semi-definite programming (SDP). Simulation and measurement experiments conducted with automotive radars underscore the remarkable performance of our proposed method. © The Institution of Engineering & technology 2023.

关键词： Automotive radar

来源：评论

学校读者我要写书评

暂无评论

A cloaking-based approach to protect location privacy in location-based services 33

A cloaking-based approach to protect location privacy in loc...

引用

Proceedings of the 33rd Chinese Control Conference, CCC 2014

作者： Zheng, Jiangyu Tan, Xiaobin Zou, Cliff Niu, Yukun Zhu, Jin CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230026 China Department of Automation University of Science and Technology of China Hefei230026 China Department of Electrical Engineering and Computer Science University of Central Florida OrlandoFL32816-2362 United States

ISBN: (纸本)9789881563842

With the widespread use of mobile devices, the location-based service (LBS) applications become increasingly popular, which introduces the new security challenge to protect user's location privacy. On one hand, a user expects to report his own location as far as possible away from his real location to protect his location privacy. On the other hand, in order to obtain high quality of service (QoS), users are required to report their locations as accurate as possible. To achieve the dedicated tradeoff between privacy requirement and QoS requirement, we propose a novel approach based on cloaking technique. We also discuss the disadvantage of the traditional general system model and propose an improved model. The basic idea of our approach is to select a sub-area from the generated cloaking area as user's reported location. The sub-area may not contain a user's real location, which prevents an adversary from performing attack with side information. Specifically, by defining an objective function with a novel location privacy metric and a QoS metric, we are able to convert the privacy issue to an optimization problem. Then, location privacy metric and QoS metric are given. To reduce the complexity of the optimization, a heuristic algorithm is proposed. Through privacy-preserving analysis and comparison with related work [8], we demonstrate the effectiveness and efficiency of our approach. © 2014 TCCT, CAA.

关键词： Location based services

来源：评论

学校读者我要写书评

暂无评论

AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH application TO TOMOSAR IMAGING

AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH APP...

引用

IET International Radar Conference 2023, IRC 2023

作者： Wang, Muhan Gao, Silin Zhang, Zhe 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 Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781839539954

Synthetic aperture radar (SAR) tomography (TomoSAR) has garnered significant attention due to its capability for three-dimensional reconstruction. Compressed sensing (CS) methods are widely employed to address the TomoSAR inversion challenge. Nevertheless, practical applications reveal phase errors among different channels, resulting in defocusing and blurring when relying solely on CS for 3D reconstruction. Current state-of-the-art autofocus techniques suffer from prohibitive computational complexity, limiting their applicability to large-scale 3D imaging. In pursuit of efficient TomoSAR 3-D autofocusing, we proposed ASAMP-Net, an innovative deep unfolding network. Operating within a two-step framework, each layer comprises two stages: phase error estimation and iterative scattering coefficient reconstruction using the sparse adaptive matching pursuit (SAMP) algorithm. Additionally, phase error estimation is obtained through mathematical derivation, while challenges associated with fixed sparsity and limited efficiency in conventional methods are mitigated through deep learning techniques. Simulation experiments and real data validation affirm the effectiveness and superiority of the proposed method. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

来源：评论

学校读者我要写书评

暂无评论

GRIDLESS TOMOGRAPHIC SAR IMAGING BASED ON ACCELERATED ATOMIC NORM MINIMIZATION WITH EFFICIENCY

GRIDLESS TOMOGRAPHIC SAR IMAGING BASED ON ACCELERATED ATOMIC...

引用

2022 International Conference on Radar systems, RADAR 2022

作者： Gao, Silin Zhang, Zhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China

ISBN: (纸本)9781839537776

Synthetic aperture radar (SAR) tomography (TomoSAR) enables the reconstruction and three-dimensional (3D) localization of targets based on multiple two-dimensional (2D) observations of the same scene. The resolving along the elevation direction can be treated as a line spectrum estimation problem. However, traditional super-resolution spectrum estimation algorithms require multiple snapshots and uncorrelated targets. Meanwhile, as the most popular TomoSAR imaging method in modern years, compressed sensing (CS) based methods suffer from the gridding mismatch effect which markedly degrades the imaging performance. As a gridless CS approach, atomic norm minimization can avoid the gridding effect but requires enormous computing resources. Addressing the above issues, this paper proposes an improved fast ANM algorithm to TomoSAR elevation focusing by introducing the IVDST-ANM algorithm, which reduces the huge computational complexity of the conventional time-consuming semi-positive definite programming (SDP) by the iterative Vandermonde decomposition and shrinkage-thresholding (IVDST) approach, and retains the benefits of ANM in terms of gridless imaging and single snapshot recovery. We conducted experiments using simulated data to evaluate the performance of the proposed method, and reconstruction results of an urban area from the SARMV3D-Imaging 1.0 dataset are also presented. © 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

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：