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Sub-pixel change detection for urban land-cover analysis via multi-temporal remote sensing images

Geo-Spatial information Science 2014年第1期17卷 26-38页

作者： Peijun DU Sicong LIU Pei LIU Kun TAN Liang CHENG Key Laboratory for Satellite Mapping Technology and Application of National Administration of Surveying Mapping and GeoinformationNanjing UniversityNanjing CityJiangsu ProvinceP.R.China Department of Information Engineering and Computer Science University of TrentoTrentoItaly Jiangsu Provincial Key Laboratory for Resources and Environment Information Engineering China University of Mining and TechnologyXuzhouJiangsu ProvinceP.R.China

Conventional change detection approaches are mainly based on per-pixel processing,which ignore the sub-pixel spectral variation resulted from spectral *** for medium-resolution remote sensing images used in urban landcover change monitoring,land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial ***,traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably,degrading the overall accuracy of change *** order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level,a novel change detection scheme is designed based on the spectral mixture analysis and decision-level *** spectral mixture model is selected for spectral unmixing,and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple composition *** proposed method is tested on multi-temporal Landsat Thematic Mapper and China–Brazil Earth Resources Satellite remote sensing images for the land-cover change detection over urban *** effectiveness of the proposed approach is confirmed in terms of several accuracy indices in contrast with two pixel-based change detection methods(*** vector analysis and principal component analysis-based method).In particular,the proposed sub-pixel change detection approach not only provides the binary change information,but also obtains the characterization about change direction and intensity,which greatly extends the semantic meaning of the detected change targets.

关键词： change detection sub-pixel level processing multi-temporal images spectral mixture model back propagation neural network remote sensing

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Quotient space granularity selection based Affinity Propagation clustering algorithm

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Journal of Computational information Systems 2014年第6期10卷 2425-2433页

作者： Ding, Shifei Li, Hui School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Jiangsu Normal University Xuzhou 221116 China

The aim of this paper is to develop an improved AP clustering algorithm based on the quotient space granularity selection. Firstly, we give the characteristics of the quotient space granularity and Affinity Propagation (AP) clustering. Secondly, we analyze the interlinking of clustering and the quotient space granularity. And then we introduce the quotient space concept to the AP clustering analysis, which can find an optimal granularity from all possible granularities. Simulation results show that our proposed method here, quotient space granularity selection based AP clustering algorithm, is effective and feasible and has a better performance. Copyright © 2014 Binary information Press.

关键词： Clustering algorithms

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Estimation of distribution algorithms for knapsack problem

Journal of Software

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Journal of Software 2014年第1期9卷 104-110页

作者： Gao, Shang Qiu, Ling Cao, Cungen School of Computer Science and Technology Jiangsu University of Science and Technology Zhenjiang 212003 China Artificial Intelligence of Key Laboratory of Sichuan Province Sichuan University of Science and Engineering Zigong 643000 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

Estimation of distribution algorithms (EDAs) is a new kind of evolution algorithm. In EDAs, through the statistics of the information of selected individuals in current group, the probability of the individual distribution in next generation is given and the next generation of group is formed by random sampling. A wide range of mathematical model of the knapsack problem are proposed. In this paper, the EDAs is applied to solve the knapsack problem. The influence of several strategies, such as numbers of population and better population selection proportions are analyzed. Simulation results show that the EDAs is reliable and effective for solving the knapsack problem. The Maltab code is given also. It can easily be modified for any combinatorial problem for which we have no good specialized algorithm. © 2014 ACADEMY PUBLISHER.

关键词： Genetic algorithms

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International Joint Conference on Neural Networks

作者： Chuntao Ding Li Zhang Yaping Lu Shuping He School of Computer Science and Technology & Provincial Key Laboratory for Computer Information Processing School of Computer Science and Technology Provincial Key Laboratory for Computer Information Processing

ISBN: (纸本)9781479914821

The idea that with the help of proper dimensionality reduction, trying to make the samples with the same label be compact and the ones with the different labels be separate after projection, is introduced into classification problems with high-dimensional data. Based on the analysis of the drawbacks of Discriminant Neighborhood Embedding (DNE) and Locality-Based Discriminant Neighborhood Embedding (LDNE), being the two relatively successful Locally Discriminant Analysis methods proposed in recent years, this paper proposes a method called Similarity-balanced Discriminant Neighborhood Embedding (SBDNE). When constructing the adjacent graph, SBDNE fully takes into account the geometric construction of manifold and the problem of imbalance between the intra-class points and the inter-class points. By endowing these two kinds of samples with different similarities and selecting the near neighbors according to the similarity matrix, not only the structure in the original space can be preserved more efficiently, but also the choice of discriminative information increases. The method proposed here has a better recognition with comparisons to some classical methods, which fully shows that SBDNE method has the capacity to efficiently solve the classification problem.

关键词： Discriminant neighborhood embedding Adjacent graph Intra-class Inter-class

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A novel hybrid intelligence algorithm for solving combinatorial optimization problems

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Journal of Computing Science and Engineering 2014年第4期8卷 199-206页

作者： Deng, Wu Chen, Han Li, He Software Institute Dalian Jiaotong University Dalian China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China The Artificial Intelligence Key Laboratory of Sichuan Province Sichuan University of Science and Engineering Zigong China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for Nationalities Nanning China

The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity. © 2014. The Korean Institute of information Scientists and Engineers.

关键词： Genetic algorithms

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A decoupled hybrid structure for active noise control with uncorrelatednarrowband disturbances 43

A decoupled hybrid structure for active noise control with u...

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43rd International Congress on Noise Control Engineering: Improving the World Through Noise Control, INTERNOISE 2014

作者： Wu, Lifu Qiu, Xiaojun Burnett, Ian S. Cheng, Eva Guo, Yecai Royal Melbourne Institute of Technology School of Electrical and Computer Engineering Australia Jiangsu Key Laboratory of Meteorological Observation and Information Processing Nanjing University of Information Science and Technology 210044 China

ISBN: (纸本)9780909882037

In real active noise control (ANC)applications,the following situations frequently occur, one isthat disturbances only present at the error sensor and havelowcorrelation with reference signal, the other is thatthere is no enough space or ideal position for locating the reference sensor to satisfy causality condition. Thusthe residual noise after feedforward control can be seen as uncorrelated narrowband disturbancesin these situationsand ahybrid adaptive feedforward and feedback structure is often utilized to cope with this *** efforts have been paid to improve the performance of the hybrid ANC system, nevertheless, few interests are concerned about the combination method between the feedforward and feedback structure. After investigating the conventional combination method of hybrid feedforward and feedback system, this paper introduces analternate combination method for hybrid ANC systemwhich featuresthat itavoidsthe coupling between the feedforward and feedback structures and both structures are concatenated to attenuate the ambient noise. Simulations are carried out to validatethe effectiveness of the introduced methodfor ANCwith uncorrelated narrowband disturbances.

关键词： Hybrid systems

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A unified semi-supervised framework for author disambiguation in academic social network

A unified semi-supervised framework for author disambiguatio...

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25th International Conference on Database and Expert Systems Applications, DEXA 2014

作者： Wang, Peng Zhao, Jianyu Huang, Kai Xu, Baowen School of Computer Science and Engineering Southeast University China State Key Laboratory for Novel Software Technology Nanjing University China State Key Laboratory of Software Engineering Wuhan University China Provincial Key Laboratory for Computer Information Processing Technology Soochow University China

ISBN: (纸本)9783319100845

This paper addresses the author disambiguation problem in academic social network, namely, resolves the phenomenon of synonym problem "multiple names refer to one person" and polysemy problem "one name refers to multiple persons". A unified semi-supervised framework is proposed to deal with both the synonym and polysemy problems. First, the framework uses semi-supervised approach to solve the cold-start problem in author disambiguation. Second, robust training data generating method based on multi-aspect similarity indicator is used and a way based on support vector machine is employed to model different kinds of feature combinations. Third, a self-taught procedure is proposed to solve ambiguity in coauthor information to boost the performances from other models. The proposed framework is verified on a large-scale real-world dataset, and obtains promising results. © 2014 Springer International Publishing Switzerland.

关键词： Semantics

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Research and application analysis of feature binding mechanism 8

Research and application analysis of feature binding mechani...

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8th IFIP International Conference on Intelligent information processing, IIP 2014

作者： Han, Youzhen Ding, Shifei School of Computer Science and Technology China University of Mining and Technology XuzhouJiangsu221116 China Key Laboratory of Intelligence Information Processing Institute of Computer Technology Academy of Sciences Beijing City100190 China

ISBN: (纸本)9783662449790

In order to recognize the object of the external world, brain need to integrate the information of different cortical areas, then form a complete world. And binding problem study on a process to percept a complete object by integrating information, which scattering on different cortical areas. As a central problem of cognitive science and neuroscience, the concept of feature binding is becoming to the focus of consciousness argument. At the beginning of this paper, we introduced the concept, characteristics and theory source of feature binding. And according to the main theoretical research methods of this mechanism, combining the latest advance at home and abroad, we made a systematical review of the research situation of feature binding problem applying in perceptual learning. At last, we pointed out the key point of the further study, which may be refined research of bundled brain mechanism on different cognition process and systematical study of general bundled brain mechanism. © IFIP International Federation for information processing 2014.

关键词： Brain

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Hidden space discriminant neighborhood embedding

Hidden space discriminant neighborhood embedding

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International Joint Conference on Neural Networks (IJCNN)

作者： Chuntao Ding Li Zhang Bangjun Wang School of Computer Science and Technology & Provincial Key laboratory for Computer Information Processing Soochow University Suzhou Jiangsu China

Discriminant neighborhood embedding (DNE) algorithm is one of supervised linear dimensionality reduction methods. Its nonlinear version kernel discriminant neighborhood embedding (KDNE) is expected to behave well on classification tasks. However, since KDNE constructs an adjacent graph in the original space, the adjacency graph could not represent the adjacent information in the kernel mapping space. By introducing hidden space, this paper proposes a novel nonlinear method for DNE, called hidden space discriminant neighborhood embedding (HDNE). This algorithm first maps the data in the original space into a high dimensional hidden space by a set of nonlinear hidden functions, and then builds an adjacent graph incorporating neighborhood information of the dataset in the hidden space. Finally, DNE is used to find a transformation matrix which would map the data in the hidden space to a low-dimensional subspace. The proposed method is applied to ORL face and MNIST handwritten digit databases. Experimental results show that the proposed method is efficiency for classification tasks.

关键词： Joints Conferences Neural networks

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Sparse belief propagation algorithm based on latent dirichlet allocation

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Diangong Jishu Xuebao/Transactions of China Electrotechnical Society 2015年 30卷 506-511页

作者： Tu, Xionggang Chen, Jun Yang, Lu Yan, Jianfeng School of Design and Arts Zhejiang Industry Polytechnic College ShaoxingZhejiang312000 China School of Computer Science and Technology Soochow University Suzhou215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China

Latent Dirichlet allocation(LDA) is a popular and unsupervised tool for reducing dimension, has been applied in text mining and information retrieval. Belief propagation is competitive in both speed and accuracy compared with other inference algorithms of LDA. In this paper, we propose belief propagation algorithm with sparseness constraints and use L1-norm, L2-norm to measure the sparseness of vector. In iteration, we project the topic-word distribution to a sparse space for getting better semantic representation. Experimental results show that sparse belief propagation algorithm improves the accuracy of text clustering and classification than belief propagation, Gibbs sampling and nonnegative matrix factorization. ©, 2015, The editorial office of Transaction of China Electrotechnical Society. All right reserved.

关键词： Belief propagation

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