检索结果-内蒙古大学图书馆

Article dgfnet: Dual gate fusion network for land cover classification in very high-resolution images

Remote Sensing 2021年第18期13卷

作者： Guo, Yongjie Wang, Feng Xiang, Yuming You, Hongjian The Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 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

Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on land cover classification thanks to their outstanding nonlinear feature extraction abil-ity. DCNNs are usually designed as an encoder–decoder architecture for the land cover classification in very high-resolution (VHR) remote sensing images. The encoder captures semantic representation by stacking convolution layers and shrinking image spatial resolution, while the decoder restores the spatial information by an upsampling operation and combines it with different level features through a summation or skip connection. However, there is still a semantic gap between different-level features;a simple summation or skip connection will reduce the performance of land-cover classification. To overcome this problem, we propose a novel end-to-end network named Dual Gate Fusion Network (DGFNet) to restrain the impact of the semantic gap. In detail, the key of DGFNet consists of two main components: Feature Enhancement Module (FEM) and Dual Gate Fusion Module (DGFM). Firstly, the FEM combines local information with global contents and strengthens the feature representation in the encoder. Secondly, the DGFM is proposed to reduce the semantic gap between different level features, effectively fusing low-level spatial information and high-level semantic information in the decoder. Extensive experiments conducted on the LandCover dataset and the ISPRS Potsdam dataset proved the effectiveness of the proposed network. The DGFNet achieves state-of-art performance 88.87% MIoU on the LandCover dataset and 72.25% MIoU on the ISPRS Potsdam dataset. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Rethinking the key Factors for the Generalization of Remote Sensing Stereo Matching Networks

arXiv

引用

arXiv 2024年

作者： Jiang, Liting Wang, Feng Zhang, Wenyi Li, Peifeng You, Hongjian Xiang, Yuming Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Target Cognition and Application Technology Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Stereo matching, a critical step of 3D reconstruction, has fully shifted towards deep learning due to its strong feature representation of remote sensing images. However, ground truth for stereo matching task relies on expensive airborne LiDAR data, thus making it difficult to obtain enough samples for supervised learning. To improve the generalization ability of stereo matching networks on cross-domain data from different sensors and scenarios, in this paper, we dedicate to study key training factors from three perspectives. (1) For the selection of training dataset, it is important to select data with similar regional target distribution as the test set instead of utilizing data from the same sensor. (2) For model structure, cascaded structure that flexibly adapts to different sizes of features is preferred. (3) For training manner, unsupervised methods generalize better than supervised methods, and we design an unsupervised early-stop strategy to help retain the best model with pre-trained weights as the basis. Extensive experiments are conducted to support the previous findings, on the basis of which we present an unsupervised stereo matching network with good generalization performance. We release the source code and the datasets at https://***/Elenairene/RKF_RSSM to reproduce the results and encourage future work. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

The Z-eigenpairs of orthogonally diagonalizable symmetric tensors

arXiv

引用

arXiv 2021年

作者： Wang, Lei Geng, Xiurui Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China University of the Chinese Academy of Sciences Beijing100049 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Science Beijing100190 China

In this paper, we focus on a special class of symmetric tensors, which can be orthogonally diagonalizable, and investigate their Z-eigenpairs problem. We show that the eigenpairs can be uniformly expressed using several basic eigenpairs, and the number of all the eigenpairs is uniquely determined by the order and rank of the symmetric tensor. In addition, we exploit the local optimality of each eigenpair by checking the second-order necessary condition. © 2021, CC BY.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Recent Advance in Content-based Image Retrieval: A literature survey

arXiv

引用

arXiv 2017年

作者： Zhou, Wengang Li, Houqiang Fellow, Tian I.E.E.E. CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Department of Computer Science University of Texas San Antonio San AntonioTX78249 United States

—The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content-based image retrieval (CBIR), which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content-based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research. Copyright © 2017, The Authors. All rights reserved.

关键词： Content based retrieval

来源：评论

学校读者我要写书评

暂无评论

Outage Analysis of Dual-Hop Transmission with Buffer Aided Amplify-and-Forward Relay

Outage Analysis of Dual-Hop Transmission with Buffer Aided A...

引用

IEEE Vehicular technology Conference

作者： Guan-Xing Li Chen Dong Dong Liu Guangtao Li Yonghui Zhang China Mobile Group Liaoning Ltd. School of ECS University of Southampton CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China RF Micro Devices Co. Ltd.

ISBN: (纸本)9781479944484

A new packet-selection based transmission scheme is proposed on a dual-hop link with buffer aided amplify-and-forward relay, where the relay with a buffer can record the channel information of source-relay hop and store the received packets. In order to satisfy the constraint of equivalent end-to-end SNR, the relay may pick up a packet in the buffer to transmit, with consideration of the channel statuses of both the source-relay hop and the relay-destination hop. Therefore, by adjusting the packet transmission order in the relay, the outage probability can be decreased in the proposed transmission scheme. Both theoretical analyses and simulation results show that the proposed scheme outperforms traditional ones by a significant margin of 2-4 dB gain in terms of outage probability.

关键词： Relays Signal to noise ratio Fading Tin Delays Wireless communication Buffer storage

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Stereo Matching Network for VHR Remote Sensing Images Based on Error Prediction

Unsupervised Stereo Matching Network for VHR Remote Sensing ...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Liting Jiang Yuming Xiang Feng Wang Hongjian You Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Stereo matching in remote sensing has recently garnered increased attention, primarily focusing on supervised learning. However, datasets with ground truth generated by expensive airbone Lidar exhibit limited quantity and diversity, constraining the effectiveness of supervised networks. In contrast, unsupervised learning methods can leverage the increasing availability of very-high-resolution (VHR) remote sensing images, offering considerable potential in the realm of stereo matching. Motivated by this intuition, we propose a novel unsupervised stereo matching network for VHR remote sensing images. A light-weight module to bridge confidence with predicted error is introduced to refine the core model. Robust unsupervised losses are formulated to enhance network convergence. The experimental results on US3D and WHU-Stereo datasets demonstrate that the proposed network achieves superior accuracy compared to other unsupervised networks and exhibits better generalization capabilities than supervised models. Our code will be available at https://***/Elenairene/CBEM.

关键词： Bridges Laser radar Codes Supervised learning Focusing Estimation Predictive models

来源：评论

学校读者我要写书评

暂无评论

Fault-Tolerant Cooperative Tracking Control of Nonlinear Affine systems Using Integral Sliding Mode Control

Fault-Tolerant Cooperative Tracking Control of Nonlinear Aff...

引用

IEEE International Symposium on Industrial Electronics

作者： Qichao Ma Jiahu Qin Yu Kang Wei Xing Zheng Department of Automation University of Science and Technology of China Hefei China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beiiinz China School of Computing Engineering and Mathematics Western Sydney University Sydney Australia

The cooperative tracking problem for a class of nonlinear affine systems are addressed. Actuator faults and external disturbance/model uncertainty are allowed such that we aim to design distributed controllers that can tolerate them while realize cooperative tracking. A general setup is considered, that is, actuator faults can take such forms as actuator degradation and total failure. Then, to guarantee the feasibility of fault-tolerant control, we assume that the actuator has sufficiently many healthy components which enables us to apply integral sliding mode control (ISMC) to develop the distributed controllers that can fulfill the control target and the external disturbance can simultaneously be successfully rejected. We also provide the simulation results to verify the validity of our theoretical findings.

关键词： Cooperative tracking Fault-tolerant control Nonlinear affine systems Integral sliding mode control

来源：评论

学校读者我要写书评

暂无评论

Position detection and direction prediction for arbitrary-oriented ships via multiscale rotation region convolutional neural network

arXiv

引用

arXiv 2018年

作者： Fu, Kun Guo, Zhi Yan, Menglong Sun, Xian Sun, Hao Yang, Xue Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

—Ship detection is of great importance and full of challenges in the field of remote sensing. The complexity of application scenarios, the redundancy of detection region, and the difficulty of dense ship detection are all the main obstacles that limit the successful operation of traditional methods in ship detection. In this paper, we propose a brand new detection model based on multiscale rotational region convolutional neural network to solve the problems above. This model is mainly consist of five consecutive parts: Dense Feature Pyramid Network (DFPN), adaptive region of interest (ROI) Align, rotational bounding box regression, prow direction prediction and rotational nonmaximum suppression (R-NMS). First of all, the low-level location information and high-level semantic information are fully utilized through multiscale feature networks. Then, we design adaptive ROI Align to obtain high quality proposals which remain complete spatial and semantic information. Unlike most previous approaches, the prediction obtained by our method is the minimum bounding rectangle of the object with less redundant regions. Therefore, rotational region detection framework is more suitable to detect the dense object than traditional detection model. Additionally, we can find the berthing and sailing direction of ship through prediction. A detailed evaluation based on SRSS and DOTA dataset for rotation detection shows that our detection method has a competitive performance. Copyright © 2018, The Authors. All rights reserved.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Electromagnetic Simulation Aided SAR Target Classification Via Deep Domain Adaptation

Electromagnetic Simulation Aided SAR Target Classification V...

引用

IEEE International Conference on Radar

作者： Xiaoling Lv Xiaolan Qiu Wenming Yu Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Suzhou Aerospace Information Research Institute Suzhou China State key Laboratory of Millimeter Waves Southeast University Nanjing China

ISBN: (纸本)9781665468893

Convolutional neural networks (CNNs) have made tremendous success in optical images classification recently. However, in synthetic aperture radar (SAR) target classification, it is difficult to annotate a large amount of real SAR images to train CNNs. Sufficient annotated images can be easily obtained through simulation, but the disparity between the simulated images and the real images makes them difficult to directly apply to the real images classification. In this paper, we propose a model that integrates multi-kernel maximum mean discrepancy (MK-MMD) and domain-adversarial training to alleviate this problem. Simulated SAR images with annotation and unlabeled real SAR images are used to train our model. First, we use domain-adversarial training to prompt the model to extract domain-invariant features. Then, the MK-MMD between the hidden representations of simulated images and real images is reduced to narrow domain discrepancy. Experimental results on the real SAR dataset demonstrate that our method effectively solves the domain shift problem and improves the classification accuracy.

关键词： Training Adaptation models Optical fiber networks Feature extraction Optical imaging Radar polarimetry Data models

来源：评论

学校读者我要写书评

暂无评论

Miniaturization Design of TEM Horn Antenna for Ground-Penetrating Radar

Miniaturization Design of TEM Horn Antenna for Ground-Penetr...

引用

International Conference on Microwave and Millimeter Wave technology Proceedings

作者： Zhi-Hao Li Jing Ma Jin Wu Jia-Jia Huang Yong-Han Lu Lin Peng Key Laboratory of Cognitive Radio and Information Processing (Ministry of Education) Guilin University of Electronic Technology Guilin China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Microwave and Optical Wave Application Technology (Guilin University of Electronic Technology) Guilin China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing China

This paper presents a miniaturized TEM horn antenna for ground penetrating radar (GPR). First, an exponential gradient horn antenna is designed with a low cut-off frequency of 0.45 GHz. Then, the low cut-off frequency is decreased to 0.19 GHz through curve structural loading and lens loading without increasing the size of the horn. The antenna size is 378.49 mm × 359.07 mm × 386.06 mm, corresponding to 0.226 λ 1 × 0.238 λ 1 × 0.243 λ 1 ( λ 1 is the wavelength corresponding to the lowest frequency of the antenna). The results show that the working bandwidth of the antenna is 0.19-3GHz, and it has a good radiation pattern in the band, with a peak gain of 12.4dBi, which would be a good candidate for GPR.

关键词：

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：