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IEEE International Conference on Image processing

作者： Changyue Ma Dong Liu Xiulian Peng Feng Wu CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China Microsoft Research Asia Beijing China

In the state-of-the-art video coding standard-High Efficiency Video Coding (HEVC), context-adaptive binary arithmetic coding (CABAC) is adopted as the entropy coding tool. In CABAC, the binarization processes are manually designed, and the context models are empirically crafted, both of which incur that the probability distribution of the syntax elements may not be estimated accurately, and restrict the coding efficiency. In this paper, we adopt a convolutional neural network-based arithmetic coding (CNNAC) strategy, and conduct studies on the coding of the DC coefficients for HEVC intra coding. Instead of manually designing binarization process and context model, we propose to directly estimate the probability distribution of the value of the DC coefficient using densely connected convolutional networks. The estimated probability together with the real DC coefficient are then input into a multi-level arithmetic codec to fulfill entropy coding. Simulation results show that our proposed CNNAC leads to on average 22.47% bits saving compared with CABAC for the bits of DC coefficients, which corresponds to 1.6% BD-rate reduction than the HEVC anchor.

关键词： Context modeling Probability distribution Entropy coding Codecs Training data Tools

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Identity Regularized Sparse Representation for Automatic Target Recognition in Sar Images

Identity Regularized Sparse Representation for Automatic Tar...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Zongxu Pan Lei Liu Bin Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

An identity regularized sparse representation (IRSR) based SAR target recognition method is proposed in this paper. The method aims to find a transformation that can map the data to a transformed space, in which targets from the same class are close with each other, no matter the distance of them in the original space. This identity constraint can be formulated as a ℓ 1 -norm minimization problem. By decoupling the problem into the sparse coding problem and the dictionary learning problem, the solution can be obtained iteratively. The solution is simply the weighted average of the sparse coding of all training data. Experimental results demonstrate that the proposed method is superior to several related methods.

关键词： Synthetic aperture radar Target recognition Linear programming Airplanes Remote sensing Support vector machines Minimization

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Layer-wise coordination between encoder and decoder for neural machine translation 18

Layer-wise coordination between encoder and decoder for neur...

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Proceedings of the 32nd International Conference on Neural information processing systems

作者： Tianyu He Xu Tan Yingce Xia Di He Tao Qin Zhibo Chen Tie-Yan Liu CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Microsoft Research Key Laboratory of Machine Perception MOE School of EECS Peking University

Neural Machine Translation (NMT) has achieved remarkable progress with the quick evolvement of model structures. In this paper, we propose the concept of layer-wise coordination for NMT, which explicitly coordinates the learning of hidden representations of the encoder and decoder together layer by layer, gradually from low level to high level. Specifically, we design a layer-wise attention and mixed attention mechanism, and further share the parameters of each layer between the encoder and decoder to regularize and coordinate the learning. Experiments show that combined with the state-of-the-art Transformer model, layer-wise coordination achieves improvements on three IWSLT and two WMT translation tasks. More specifically, our method achieves 34.43 and 29.01 BLEU score on WMT16 English-Romanian and WMT14 English-German tasks, outperforming the Transformer baseline.

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A DESIGN OF THE COMPLETE POLARIZATION CONVERTER USING DIELECTRIC PERIODIC STRUCTURES

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

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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

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Unsupervised classification of polarimetric circular SAR images based on H/α/Multi-aperture polarimetric entropy 12

Unsupervised classification of polarimetric circular SAR ima...

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12th European Conference on Synthetic Aperture Radar, EUSAR 2018

作者： Hong, Wen Xue, Feiteng Lin, Yun Chen, Shiqiang Shen, Wenjie University of Chinese Academy of Sciences Beijing China Institute of Electronics Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geospatial Information Processing and Application System Beijing China

ISBN: (纸本)9783800746361

In conventional polarimetric synthetic aperture radar(PolSAR), targets are usually assumed isotropic and potential polarimetric variations across azimuth are unconsidered. As to circular SAR (CSAR), the azimuthal view angle is much larger. Polarimetric variations across azimuth are no longer negligible. H/α unsupervised classification method classifies data by polarimetric properties. H/α method can not distinguish anisotropic and isotropic targets which have same polarimetric properties when applied to polarimetric CSAR(Pol-CSAR) data. Recently multi-aperture polarimetric entropy(MAPE) was proposed in Ref. [10]. MAPE can be used to identify anisotropic and isotropic targets. In this paper, an unsupervised classification method based on H/α/MAPE is proposed. The proposed classification method classifies data with H/α/MAPE space. Anisotropic and isotropic targets which have same polarimetric properties are well classified into two classes by H/α/MAPE method. The effectiveness and advantages of H/α/MAPE method are demonstrated on Pol-CSAR data, acquired from the Institute of Electronics airborne CSAR system at P-band. © VDE VERLAG GMBH Â Berlin Â Offenbach.

关键词： Synthetic aperture radar

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NTIRE 2020 Challenge on Image and Video Deblurring

arXiv

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arXiv 2020年

作者： Seungjun, Nah Sanghyun, Son Radu, Timofte Kyoung Mu, Lee Tseng, Yu Xu, Yu-Syuan Chiang, Cheng-Ming Tsai, Yi-Min Brehm, Stephan Scherer, Sebastian Xu, Dejia Chu, Yihao Sun, Qingyan Jiang, Jiaqin Duan, Lunhao Yao, Jian Purohit, Kuldeep Suin, Maitreya Rajagopalan, A.N. Ito, Yuichi Hrishikesh, P.S. Puthussery, Densen Akhil, K.A. Jiji, C.V. Kim, Guisik Deepa, P.L. Xiong, Zhiwei Huang, Jie Liu, Dong Kim, Sangmin Nam, Hyungjoon Kim, Jisu Jeong, Jechang Huang, Shihua Fan, Yuchen Yu, Jiahui Yu, Haichao Huang, Thomas S. Zhou, Ya Li, Xin Liu, Sen Chen, Zhibo Dutta, Saikat Das, Sourya Dipta Garg, Shivam Sprague, Daniel Patel, Bhrij Huck, Thomas Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland MediaTek Inc University of Augsburg Chair for Multimedia Computing and Computer Vision Lab Germany Peking University China Beijing University of Posts and Telecommunications China Beijing Jiaotong University China Wuhan University China Indian Institute of Technology Madras India Vermilion College of Engineering Trivandrum India CVML Chung-Ang University Korea Republic of APJ Abdul Kalam Technological University India University of Science and Technology of China China Image Communication Signal Processing Laboratory Hanyang University Korea Republic of Southern University of Science and Technology China University of Illinois at Urbana-Champaign United States CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China China IIT Madra Jadavpur University India University of Texas Austin United States Duke University Computer Science Department United States

Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. In this challenge, we present the evaluation results from 3 competition tracks as well as the proposed solutions. Track 1 aims to develop single-image deblurring methods focusing on restoration quality. On Track 2, the image deblurring methods are executed on a mobile platform to find the balance of the running speed and the restoration accuracy. Track 3 targets developing video deblurring methods that exploit the temporal relation between input frames. In each competition, there were 163, 135, and 102 registered participants and in the final testing phase, 9, 4, and 7 teams competed. The winning methods demonstrate the state-of-the-art performance on image and video deblurring tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Image enhancement

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Analysis and Prediction of Vehicle Exhaust Emission Using ANN 36

Analysis and Prediction of Vehicle Exhaust Emission Using AN...

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第36届中国控制会议

作者： Zhenyi Xu Yu Kang Wenjun Lv Department of Automation and State Key Laboratory of Fire Science 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 focused on the analysis of vehicle emission based on the Hefei remote sensing data during the last three *** we propose a three-layer artificial neural network model for predicting vehicle exhaust emission using remote sensing ***,we take adaptive-lasso algorithm to analyze the various factors from the emission data,and determine the principal ***,after doing principal components analysis and selecting algorithm and architecture,the Back-Propagation neural network model with 7-12-1 architecture was established as the optimal ***,we give the prediction results on the testing data-set and prove the potentiality and validity of the proposed method in the prediction of vehicle exhaust emission.

关键词： Vehicle Exhaust Emission Adaptive-Lasso Artificial Neural Network

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A fundamental theorem for eco-environmental surface modelling and its applications

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Science China Earth Sciences 2020年第8期63卷 1092-1112页

作者： Tianxiang YUE Na ZHAO Yu LIU Yifu WANG Bin ZHANG Zhengping DU Zemeng FAN Wenjiao SHI Chuanfa CHEN Mingwei ZHAO Dunjiang SONG Shihai WANG Yinjun SONG Changqing YAN Qiquan LI Xiaofang SUN Lili ZHANG Yongzhong TIAN Wei WANG Ying’an WANG Shengnan MA Hongsheng HUANG Yimin LU Qing WANG Chenliang WANG Yuzhu WANG Ming LU Wei ZHOU Yi LIU Xiaozhe YIN Zong WANG Zhengyi BAO Miaomiao ZHAO Yapeng ZHAO Yimeng JIAO Ufra NASEER Bin FAN Saibo LI Yang YANG John PWILSON State Key L aboratory of Resources and Environment Information System Institute of Geographical Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049China College of Land Resources and Environment Jiangxi Agricultural UniversityNanchang 330045China College of Geomatics Shandong University of Science and TechnologyQingdao 266590China College of Forestry Beijing Forestry UniversityBeijing 100083China School of Land and Resources China West Normal UniversityNanchong 637002China College of Geographical Information and Tourism Chuzhou UniversityChuzhou 239000China Institutes of Science and Development Chinese Academy of SciencesBeijing 100190China Department of Information Engineering Shandong University of Science and TechnologyTai'an 271019China College of Resources Sichuan Agricultural UniversityChengdu 611130China College of Geography and Tourism Qufu Normal UniversityRizhao 276828China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100101China School of Geographical Science Southwest UniversityChongqing 400715China National Disaster Reduction Center of China Ministry of Emergency ManagementBeijing 100721China Department for Population Data China Population and Development Research CenterBeijing 100081China Department for Industrial Standard China Standardization AdministrationBeijing 100088China The Academy of Digital China Fuzhou UniversityFuzhou 350002China National Institute of Environmental Health Chinese Center for Disease Control and PreventionBeijing 100021China School of Information Engineering China University of GeoscienceBeijing 100083China Qian Xuesen Laboratory of Space Technology Beijing 100094China College of Architecture and Urban Planning Chongqing Jiaotong UniversityChongqing 400074China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing

We propose a fundamental theorem for eco-environmental surface modelling(FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface system modeling(FTESM). The Beijing-Tianjin-Hebei(BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling(HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-meansquare error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM.

关键词： HASM FTEEM spatial upscaling spatial downscaling spatial interpolation Data fusion Model-data assimilation Model coupling

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A TOPSAR Calibration Method For processing system Of GF3 Next Generation

A TOPSAR Calibration Method For Processing System Of GF3 Nex...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Di Yin Bing Han Jili Sun Aiping Chen Liangbo Zhao Xinzhe Yuan Lihua Zhong Yuxin Hu University of Chinese Academy of Sciences Beijing China Institute of Electronics Chinese Academy of Sciences Beijing China Beijing Institute of Telemetry and Telecommunications Technology Beijing China Chinese Academy of Space Technology China Beijing China National Satellite Ocean Application Service State Oceanic Administration Beijing China 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

TOPSAR is an earth-imaging technique, which can provide wide swath coverage. The paper introduces a TOPSAR focusing and calibrating experiment based on the TOPSAR data acquired by Gaofen3(GF3). In this paper, we firstly derive the processor calibration factors under the demands of keeping signal energy invariant. After that, we fully analyze the impact of antenna electronic steering on TOPSAR products. Aimed to be applied to TOPSAR mode processing system of a SAR satellite, the next generation of GF3, calibration methods to processor and electronic steering was proposed in this paper.

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OBJECT DETECTION WITH HEAD DIRECTION IN REMOTE SENSING IMAGES BASED ON ROTATIONAL REGION CNN

OBJECT DETECTION WITH HEAD DIRECTION IN REMOTE SENSING IMAGE...

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IEEE International geoscience and Remote Sensing Symposium

作者： Xue Yang Kun Fu Hao Sun Xian Sun Menglong Yan Wenhui Diao Zhi Guo Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

Object detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. In this paper, we propose a novel detection framework based on rotational region convolution neural network to cope with the problem of non-maximum suppression in dense objects detection. The bounding boxes obtained by adopting our method is the minimum bounding rectangle of object with less redundant regions. Furthermore, we find the head direction of the object through prediction. There are three important changes to our framework over traditional detection methods, representation and regression of rotational bounding box, head direction prediction and rotational non-maximal suppression. Experiments based on remote sensing images from Google Earth for Object detection show that our detection method based on rotational region CNN has a competitive performance.

关键词： Object detection Rotating region Convolution neural network Prediction Non-maximal suppression

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