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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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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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Edge-Guided Panoramic Video Stitching with Limited Overlap

Edge-Guided Panoramic Video Stitching with Limited Overlap

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2019 IEEE International Conference on Signal, information and Data Processing, ICSIDP 2019

作者： Xie, Chaoyu Chen, Xuejin CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781728123455

Video stitching remains a challenging problem in computer vision. In this paper, we propose a novel edge-guided method to stitch multiple videos that have small overlapped regions. Our algorithm consists of three steps: (1) spherical projection of the input video frames based on camera calibration, (2) edge detection and edge-guided feature matching for video registration, and (3) seam optimization to eliminate distortions and ghosts in the composited panoramic videos. The experimental results and user studies demonstrate that our method is robust to videos that have small overlapped regions and produces more visually pleasing panoramic videos than state-of-the-art techniques. © 2019 IEEE.

关键词： Edge detection

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Development of land use data product at provincial level based on HJ CCD data

Development of land use data product at provincial level bas...

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International Conference on Agro-Geoinformatics

作者： Chongyang Wang Siyu Huang Dan Li Wei Liu Shuisen Chen Guangdong Open Laboratory of Geospatial Information Technology and Application Guangdong Key Laboratory of Remote Sensing and Geographical Information System Technology Application Guangzhou Institute of Geography Guangzhou Beijing China Guangdong Open Laboratory of Geospatial Information Technology and Application Guangdong Key Laboratory of Remote Sensing and Geographical Information System Technology Application Guangzhou Institute of Geography Guangzhou China Guangdong Open Laboratory of Geospatial Information Technology and Application Guangdong Key Laboratory of Remote Sensing and Geographical Information System Technology Application Guangdong Open Laboratory of Geospatial Information Technology and Application Guangdong Key Laboratory of Remote Sensing and Geographical Information System Technology Application Guangzhou China Guangdong Open Laboratory of Geospatial Information Technology and Application Guangdong Key Laboratory of Remote Sensing and Geographical Information System Technology Application Guangzhou Institute of Geography China

Land use is critical for researches about carbon accounting, environmental protection, global climate change and sustainable development, etc. Recently, land use data production could not meet researches of land use and its change in administrative regions of province-level well under the rapid economic and natural disaster growth. This paper takes Guangdong province as an example, the land use classifications were produced from HJ CCD data and LandsatLook Images with supervised classification of Maximum Likelihood. Two results were compared with the second national land survey. And confusion matrix and precision of the land use result based on two different data were evaluated. The Kappa coefficient and classification accuracy (0.74 and 82.8% for HJ CCD data, 0.69 and 78.9% for LandsatLook Images) are not very high. The area of Guangdong province is about 180,000 square kilometers. The image data used in classification are in wintertime of 2013, which is a bit different from the product of second national land survey in 2010. The result is probably reasonable when taking the factors of research area and image season and land use map time into consideration. HJ CCD data have advantages over LandsatLook Images when they were used for classifying land use types. And HJ CCD data could obtain the change of land use more effective due to high time resolution and wide coverage by two CCD sensors than LandsatLook Images. For land use mapping of whole Guangdong province, four or fewer scenes of HJ CCD images were needed. But to LandsatLook Images, it needs 15 scenes of images or more. Besides, the inner consistency of HJ CCD data is better compared with LandsatLook Images because of wide cover. In the experiment, the time spans of the four HJ CCD images is seven days while the spans of 15 LandsatLook Images is 121 days for the coverage of whole Guangdong province. Land use could stay the same almost in seven days usually. However, in 121 days, there certainly be some great

关键词： Satellites Earth Remote sensing Charge coupled devices Spatial resolution Monitoring

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Markov Decision Process Based Wireless Multicast Opportunistic Routing 33

Markov Decision Process Based Wireless Multicast Opportunist...

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第三十三届中国控制会议

作者： Ma Dianbo Tan Xiaobin Zhou Zijian Yu Shanjin Department of Automation University of Science and Technology of China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

ISBN: (纸本)9781479947249

There are more and more pervasive applications of Wireless Multicast Network, such as the video conference, the voice transmission and the software updates etc. The traditional wireless multicast routing method is to construct a multicast tree,which may ignore many available links. This paper presents Markov Decision Process Based Multicast Opportunistic Routing Model. Whether each node of the whole network has received a packet stands for different states of a Markov chains. That is to say that Markov state transition is a routing process. This paper provides a new approach for the study of wireless multicast networks routing. In the reward function of the model, the consumption of Request and ACK among the nodes is abundantly considered. In accordance with each state of the network, the network chooses the optimal forwarding nodes which make the reward function of the model maximum. The experiment and the simulation results proves MDP is most effective routing scheme,and meets with the network system's requirement.

关键词： Markov Decision Process,Opportunistic Routing,Multicast,reward function

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Social Diffusion Analysis with Common-Interest Model for Image Annotation

Social Diffusion Analysis with Common-Interest Model for Ima...

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作者： Lei, Chenyi Liu, Dong Li, Weiping Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Automatic image annotation has been extensively studied, mostly from a content-based approach, whose effectiveness is restricted by the 'semantic gap' between low-level image features and semantic annotations, and by the irrelevance of annotations to image content. We propose a social diffusion analysis approach to image annotation, which exploits abundant social diffusion records about how images are disseminated within online social networks. Specifically, we propose a common-interest model to analyze social diffusion records, with the assumption that the diffusion pattern of an image in social networks is highly related to the relevance between image annotations and user preferences. In our proposed model, user preferences are represented as common interests of pairwise users instead of individual user interests. We find the notion of common interests not only facilitates the analysis of social diffusion patterns, but also leads to more accurate profiling of user preferences compared to individual interests. Based on the common-interest model, we design an image annotation framework via social diffusion analysis, which consists of the mining of common interests from social diffusion records, the feature extraction from diffusion graphs and common interests, and the automatic annotation by the learning-to-rank method. Experimental results on real-world data sets show that our proposed common-interest based approach outperforms individual-interest based methods, and also achieves superior performance than state-of-the-art content-based image annotation methods. © 2016 IEEE.

关键词： Diffusion

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Digitizing the thermal and hydrological parameters of land surface in subtropical China using AMSR-E brightness temperatures

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International Journal of Digital Earth 2017年第7期10卷 687-700页

作者： Yongxian Su Xiuzhi Chen Hua Su Liyang Liu Jishan Liao Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and ApplicationGuangzhou Institute of GeographyGuangzhouPeople’s Republic of China Jiangsu Key Laboratory of Agricultural Meteorology College of Applied MeteorologyNanjing University of Information Science and TechnologyNanjingPeople’s Republic of China Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems South China Botanical GardenChinese Academy of SciencesGuangzhouPeople’s Republic of China Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education National Engineering Research Centre of Geo-spatial Information TechnologyFuzhou UniversityFuzhouPeople’s Republic of China

Digitizing the land surface temperature(T_(s))and surface soil moisture(m _(v))is essential for developing the intelligent Digital ***,we developed a two parameter physical-based passive microwave remote sensing model for jointly retrieving T_(s) and m_(v) using the dual-polarized T_(b) of Aqua satellite advanced microwave scanning radiometer(AMSR-E)C-band(6.9 GHz)based on the simplified radiative transfer *** using in situ T_(s) and m_(v) in southern China showed the average root mean square errors(RMSE)of T s and m_(v) retrievals reach 2.42 K(R^(2)=0.61,n=351)and 0.025 g cm^(−3)(R^(2)=0.68,n=663),*** results were also validated using global in situ T_(s)(n=2362)and m_(v)(n=1657)of International Soil Moisture *** corresponding RMSE are 3.44 k(R 2=0.86)and 0.039 g cm^(−3)(R^(2)=0.83),*** monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging Spectroradiometer T_(s)(R^(2)=0.57;RMSE=2.91 k)and ECV_SM m_(v)(R^(2)=0.51;RMSE=0.045 g cm^(−3)),***,this paper indicates an effective way to jointly modeling T_(s) and m_(v) using passive microwave remote sensing.

关键词： Surface soil moisture land surface temperature physical-based radiative transfer model AMSR-E brightness temperatures

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Human Machine Joint Decision Making in Distorted Surveillance Scenario 2

Human Machine Joint Decision Making in Distorted Surveillanc...

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2nd China Symposium on Cognitive Computing and Hybrid Intelligence, CCHI 2019

作者： Liu, Sen Zhao, Shuxin Pang, Yingxue Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781728140919

There is plenty of human-machine joint decision-making scenarios in the real world applications, such as driving assistant, suspect identification, medical diagnosis, etc. Existing algorithms propose that machine should give a rejection option when having a high risk or uncertainty score so that the input can be passed to human to make the decision. This is an interesting algorithmic model of human-machine collaboration, but implicitly assumes that humans are more trustworthy than machines. Such an assumption ignores the bias and inconsistency of human, especially in scenarios where machines have superior recognition ability than humans. In this work, we investigate the human-machine joint decision-making problem in distorted surveillance videos, where machines experimentally prove to be comparable to human beings in tolerance to distortion, sometimes even stronger. We propose a new human-machine joint decision-making framework by considering both the confidences of machine and human. To obtain the confidence of human, we build a real-life human decision-making database and propose a deep neural network to estimate human's confidence. Then, confidence alignment method and decision rule are proposed to further output the final decision. Experiments demonstrate that the proposed framework can make less human intervention and more accurate decisions in several human-machine joint decision-making scenarios. © 2019 IEEE.

关键词： Deep neural networks

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MobiNDN:A Mobility Support Architecture for NDN 33

MobiNDN:A Mobility Support Architecture for NDN

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第三十三届中国控制会议

作者： Zhou Zijian Tan Xiaobin Li Hebi Zhao Zhifan Ma Dianbo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Department of Automation University of Science and Technology of China

ISBN: (纸本)9781479947249

Named Data networking(NDN) has emerged as a new communication paradigm designed for efficient dissemination of data. However, mobility issues are not considered sufficiently. Consumer or producer mobility can incur request staleness issue, the loss of Interest and Data packets and communication delay. Through analysis, we consider how to forward the buffered data from old access point(AP) to new one and how to keep routing consistency during handoff stage are two key points to address mobility issue in NDN. To minimize the loss of Interest and Data,handoff delay during moving, we design a mobility support architecture(MobiNDN), which is a centralized system architecture and consists of initialization and three stages:registration,deletion and updating. We design three mobility scenarios and schemes and evaluate the performance of MobiNDN by comparing it against exiting NDN using extensive ndnSIM simulation. Our simulation results clearly show that MobiNDN architecture effectively decreases handoff delay and receives data packets during/after handoffs comparing with the exiting NDN.

关键词： Controller,Mobility Support,NDN

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Improving Semantic Segmentation via Label Propagation and Temporal Consistency

Improving Semantic Segmentation via Label Propagation and Te...

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2019 IEEE International Conference on Signal, information and Data Processing, ICSIDP 2019

作者： Qin, Feiyu Cao, Lumeng Chen, Xuejin CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781728123455

Semantic segmentation is a fundamental task in indoor scene understanding. Most previous supervised approaches rely on densely annotated image data sets. Due to the limited amount of images with segmentation labels, the performance of existing networks is greatly limited. In this paper, we exploit temporal correlation in video frames to improve the performance and robustness of segmentation networks. Two effective learning strategies are proposed to propagate the information from a few labeled frames to their immediate neighbor frames. First, we scale up training dataset for supervised semantic segmentation networks by generating pseudo ground-truth for neighboring frames from a labeled frame using filtered homography transformation. Furthermore, we introduce a self-supervised loss function to ensure temporal consistency between the segmentation results of adjacent frames. The experimental results demonstrate that our proposed method outperforms state-of-the-art techniques for semantic segmentation on NYU-Depth V2 dataset. © 2019 IEEE.

关键词： Semantic Segmentation

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