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arXiv 2018年

作者： Gu, Jun Xu, Guangluan Zhang, Yue Sun, Xian Wen, Ran Wang, Lei 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

Recently, deep convolutional neural networks (CNNs) have obtained promising results in image processing tasks including super-resolution (SR). However, most CNN-based SR methods treat low-resolution (LR) inputs and features equally across channels, rarely notice the loss of information flow caused by the activation function and fail to leverage the representation ability of CNNs. In this letter, we propose a novel single-image super-resolution (SISR) algorithm named Wider Channel Attention Network (WCAN) for remote sensing images. Firstly, the channel attention mechanism is used to adaptively recalibrate the importance of each channel at the middle of the wider attention block (WAB). Secondly, we propose the Local Memory Connection (LMC) to enhance the information flow. Finally, the features within each WAB are fused to take advantage of the network’s representation capability and further improve information and gradient flow. Analytic experiments on a public remote sensing data set (UC Merced) show that our WCAN achieves better accuracy and visual improvements against most state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Image fusion

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

arXiv

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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

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A Location Strategy for Traffic Emission Remote Sensing system Based on Bus Routes 36

A Location Strategy for Traffic Emission Remote Sensing Syst...

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第36届中国控制会议

作者： Yuxiao Yang Zerui Li Yu Kang Department of Automation University of Science and Technology of China State Key Laboratory of Fire Science University of Science and Technology of China Institute of Advanced Technology University of Science and Technology of China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper presents a novel location strategy for traffic emission remote sensing system（TERSS） based on bus *** the purpose of reducing cost,the corresponding Hypergraph Model is established based on graph theory and the topological structure of urban road ***,the location problem of traffic emission remote sensing detectors is defined and transformed into finding the minimum transversal of the Hypergraph which is used to obtain the location scheme for TERSS based on bus routes according to Boolean algebra ***,the proposed location strategy helps to obtain a location scheme for a city bus system to monitor buses as many as possible.

关键词： Location strategy TERSS bus routes Boolean algebra

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

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

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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

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Deep supervised qantization by self-organizing map 25

Deep supervised qantization by self-organizing map

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25th ACM International Conference on Multimedia, MM 2017

作者： Wang, Min Zhou, Wengang Tian, Qi Pu, Junfu Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China China Computer Science Department University of Texas San Antonio United States

ISBN: (纸本)9781450349062

Approximate Nearest Neighbour (ANN) search is an important research topic in multimedia and computer vision fields. In this paper, we propose a new deep supervised quantization method by Self-Organizing Map (SOM) to address this problem. Our method integrates the Convolutional Neural Networks (CNN) and Self-Organizing Map into a unified deep architecture. The overall training objective includes supervised quantization loss and classification loss. With the supervised quantization loss, we minimize the differences on the maps between similar image pairs, and maximize the differences on the maps between dissimilar image pairs. By optimization, the deep architecture can simultaneously extract deep features and quantize the features into the suitable nodes in the Self-Organizing Map. The experiments on several public standard datasets prove the superiority of our approach over the existing ANN search methods. Besides, as a byproduct, our deep architecture can be directly applied to classification task and visualization with little modification, and promising performances are demonstrated on these tasks in the experiments. © 2017 ACM.

关键词： Self organizing maps

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An interactive system for eficient 3D furniture arrangement

An interactive system for eficient 3D furniture arrangement

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2017 Computer Graphics International Conference, CGI 2017

作者： Yan, Meng Chen, Xuejin Zhou, Jie CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Dept 6 P.O. Box 4 Hefei Anhui230026 China

ISBN: (纸本)9781450352284

We present an interactive example-based system for non-expert users to generate 3D indoor scenes intuitively. From a set of examples of an interior scene, we extract furniture layout constraints including pairwise and group relationships, ergonomic factors and user habits. Instead of inserting a single furniture object to a visual pleasing and functional position independently, we take advantages of manipulating a group of functionality-related furniture objects for smart editing. To deal with the arrangement problem which requires jointly optimizing a variety of functional and visual criteria, we introduce an efficient two-step pipeline consisting of rough arrangement and layout refinement. In the rough arrangement step, furniture objects are roughly placed according to pairwise relationships for accelerating the further optimization process. In the layout refinement step, we optimize the rough furniture layout to a semantical and functional layout considering spatial relationships, and ergonomic factors and avoiding collisions. User interactions are also allowed to move a specific object or a structural group while our system automatically refines the entire layout. The experimental results demonstrate that our system for scene generation measurably increases the visual quality and time efficiency compared with the completely manual scene editing or modeling. © 2017 ACM.

关键词： Virtual reality

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Sampled-data Observer Design for a Class of Stochastic Nonlinear systems Based on the Approximate Discrete-time Models

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IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 507-511页

作者： Xinxin Fu Yu Kang Pengfei Li Department of Automation University of Science and Technology of China State Key Laboratory of Fire Science Department of AutomationInstitute of Advanced TechnologyUniversity of Science and Technology of China Key Laboratory of Technology in GeoSpatial Information Processing and Application System Chinese Academy of Sciences IEEE

In this paper,we studied the approximate sampleddata observer design for a class of stochastic nonlinear ***-Maruyama approximation was investigated in this paper because it is the basis of other higher precision numerical methods,and it preserves important structures of the nonlinear ***,the form of Euler-Maruyama model is simple and easy to be *** results provide a reference for sampled-data observer design method for such stochastic nonlinear systems,and may be useful to many practical control applications,such as tracking control in mechanical *** the effectiveness of the approach is demonstrated by a simulation example.

关键词： Approximation model exponentially bounded sampled-data observer stochastic nonlinear

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General Structure Physics of an Aerial Remote Sensing Platform and Its systemic Accuracy Criterion

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Journal of geodesy and geoinformation Science 2019年第2期2卷 2-16页

作者： Lei YAN Zhengkang ZUO Yingcheng LI Xiuxiao YUAN Yan SONG Qingsheng XUE Shihu ZHAO Institute of Remote Sensing and Geographic Information System Beijing Key Laboratory of Spatial Information Integration and Its Application Peking University Beijing 100871 China Chinese Academy of Surveying and Mapping Beijing 100039 China China TOPRS Technology Co.Ltd. Beijing 100039 China School of Remote Sensing and Information Engineering Wuhan University Wuhan 430079 China Faculty of Information Engineering China University of Geosciences Wuhan 430079 China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun 130033 China Satellite Surveying and Mapping Application Center NASG Beijing 101300 China

Accuracy is a key factor in high-resolution remote sensing and photogrammetry. The factors that affect accuracy are imaging system errors and data processing errors. Due to the complexity of aerial camera errors, this paper focuses on the design of digital aerial camera systems and the means to reduce system error and data processing inefficiencies. There are many kinds of digital aerial camera systems at present;however, these systems lack a unified physical model, which ultimately leads to more complicated designs and multi-camera modes. Such a system is complex and costly, as it is easily affected by factors such as vibration and temperature. Thus, the installed accuracy can only reach the millimeter level. Here, we describe a unified physical structure for a digital aerial camera that imitates an out-of-field multi-charge-coupled device (CCD), an in-field multi-CCD, and once-imaging and twice-imaging digital camera systems. This model is referred to as the variable baseline-height ratio spatiotemporal model. The variable ratio allows the opto-mechanical spatial parameters to be linked with height accuracy, thus providing a connection to the surface elevation. The twice-imaging digital camera prototype system and the wideband limb imaging spectrometer provide a transformation prototype from the current multi-rigid once-imaging aerial camera to a single rigid structure. Thus, our research lays a theoretical foundation and prototype references for the construction and industrialization of digital aerial systems.

关键词： catadioptric optical mirror digital aerial camera normal physical model of variable baseline-height ratio spatial temporal model single rigid structure systemic accuracy

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An improved semi-global matching method with optimized matching aggregation constraint

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IOP Conference Series: Earth and Environmental Science 2020年第1期569卷

作者： Xu Huang Yilong Han Kun Hu School of Geospatial Engineering and Science Sun Yat-sen University Rongyuan Road 4 Zhuhai China Wuhan Engineering Science and Information Institute Jiangda Road 30 Wuhan China School of Remote Sensing and Information Engineering Luoyu Road 129 Wuhan China 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 100190 Beijing China

Stereo dense image matching (DIM) is a key technique in generating dense 3D point clouds at low cost, among which semi-global matching (SGM) is one of the best compromise between the matching accuracy and the time cost. Most commercial or open-source DIM software packages therefore adopt SGM as the core algorithm for the 3D point generation, which computes matching results in 2D image space by simply aggregating the matching results of multi-directional 1D paths. However, such aggregations of SGM did not consider the disparity consistency between adjacent pixels in 2D image space, which will finally decrease the matching accuracy. To achieve higher-accuracy while keep the high time efficiency of SGM, this paper proposes an improved SGM with a novel matching aggregation optimization constraint. The core algorithm formulates the matching aggregation as the optimization of a global energy function, and a local solution of the energy function is utilized to impose the disparity consistency between adjacent pixels, which is capable of removing noises in the matching aggregation results and increasing the final matching accuracy at low time cost. Experiments on aerial image dataset show that the proposed method outperformed the traditional SGM method and another improved SGM method. Compared with the traditional SGM, our proposed method can increase the average matching accuracy by at most 11%. Therefore, our proposed method can applied in some smart 3D applications, e.g. 3D change detection, city-scale reconstruction, and global survey mapping.

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An attention-based word-level interaction model: Relation detection for knowledge base question answering

arXiv

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arXiv 2018年

作者： Zhang, Hongzhi Xu, Guandong Liang, Xiao Huang, Tinglei Fu, Kun CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Software and Advanced Analytics University of Technology Sydney Australia

Relation detection plays a crucial role in Knowledge Base Question Answering (KBQA) because of the high variance of relation expression in the question. Traditional deep learning methods follow an encoding-comparing paradigm, where the question and the candidate relation are represented as vectors to compare their semantic similarity. Max- or average- pooling operation, which compresses the sequence of words into fixed-dimensional vectors, becomes the bottleneck of information. In this paper, we propose to learn attention-based word-level interactions between questions and relations to alleviate the bottleneck issue. Similar to the traditional models, the question and relation are firstly represented as sequences of vectors. Then, instead of merging the sequence into a single vector with pooling operation, soft alignments between words from the question and the relation are learned. The aligned words are subsequently compared with the convolutional neural network (CNN) and the comparison results are merged finally. Through performing the comparison on low-level representations, the attention-based word-level interaction model (ABWIM) relieves the information loss issue caused by merging the sequence into a fixed-dimensional vector before the comparison. The experimental results of relation detection on both SimpleQuestions and WebQuestions datasets show that ABWIM achieves state-of-the-art accuracy, demonstrating its effectiveness. Copyright © 2018, The Authors. All rights reserved.

关键词： Knowledge based systems

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