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Hyperspectral data classification via sparse representation in homotopy

Hyperspectral data classification via sparse representation ...

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International Conference on Information science and Engineering (ICISE)

作者： Qazi Sami ul Haq Lixin Shi Linmi Tao Shiqiang Yang Key Laboratory of Pervasive Computing Ministry of Education Department of Computer Science and Technology Tsinghua University Beijing China

Sparse representation has significant success in many fields such as signal compression and reconstruction but to the best of our knowledge, no sparse-based classification solution has been proposed in the field of remote sensing. One of the reasons is that the general optimizers are extremely slow, time consuming and needs intensive processing for l 1 -minimization sparse representation. In this paper, we propose a fast sparse representation using l 1 -minimization based on homotopy for the classification of hyperspectral data. This method is based on the observation that a test sample can be represented by train samples from a pool of large number of train samples i.e the sparse representation. Hence the sparse representation for each test sample is achieved by the linear combination of the train samples. This proposed method has the advantages that learning on the training samples is not required, both model selection and parameter estimation are not needed, and low computational load, by which a bagging algorithm is introduced to increase the classification accuracy via voting. A real hyperspectral dataset (AVIRIS 1992 Indiana's Indian Pines image) is used to measure the performance of the proposed algorithm. We compared the accuracy results with state-of-the-art SVM and general purpose linear programming solvers. We also presented a time comparison between our approach and general LP solvers. The comparisons prove the effectiveness of the proposed approach.

关键词： Training Hyperspectral imaging Support vector machines Bagging Accuracy Equations

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A l1-minimization Based Approach for Hyperspectral Data Classification

A l1-minimization Based Approach for Hyperspectral Data Clas...

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2010 International Conference on Remote Sensing (ICRS 2010)

作者： Qazi Sami ul Haq Key Laboratory of Pervasive Computing Ministry of Education Department of Computer Science and TechnologyTsinghua UniversityBeijing 100084China

Most of classification methods require model learning procedure or optimal parameters selection using large number of training samples. In this paper, we propose a novel classification approach using l1-minimization based sparse rep- resentation which does not need any learning procedure or parameters selection. The proposed approach is based on l1- minimization because l0-minimization is generally NP-hard and is not a convex optimization problem. Sparse based solutions have been proposed in other areas like signal processing but to the best of our knowledge, this is the first time that sparse based classification is being proposed for the classification of hyperspectral data. l1-minimization based sparse representation is calculated for each of the test samples using few training samples directly. In remote sensing, usually it is extremely difficult and expensive to identify and label the samples due to which we often lack sufficient training samples for classification. We tested the proposed approach for difficult classification problem i.e high dimensional spaces and few training samples. We also analyzed the cases in which the sparse based classification may and may not work. Our extensive experiments on real hyperspectral dataset (AVIRIS 1992 Indiana’s Indian Pines image), prove that the proposed technique offers more classification accuracy and more efficient than state-of-the-art SVM and semi-supervised methods as it does not need any model selection.

关键词： Hyperspectral data, sparse,image region classi- fication,remote sensing,spectral band compression,aviris.

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A Robust Band Compression Technique for Hyperspectral Image Classification

A Robust Band Compression Technique for Hyperspectral Image ...

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2010 IEEE International Conference on Intelligent computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

作者： Qazi Samiul Haq Lixin Shi Linmi Tao Shiqiang Yang Key Laboratory of Pervasive Computing Ministry of Education Department of Computer Science and TechnologyTsinghua UniversityBeijing 100084China

Dimension reduction is the key step of hyperspectral image classification. Many techniques have been developed in the past years, but our classification experiments show that some of these techniques are not robust while others suffer from the accuracy and the effectiveness for the classification of hyperspec teal data. In this paper, a novel band compression algorithm is proposed based on the fusion of segmented principle component analysis (SPCA) and linear discriminant analysis (LDA) for dimension reduction. We first select the bands independently via SPCA and LDA. Theoretical analysis shows that the selected bands have little correlation, and therefore, an iterative algorithm is adopt to adaptively co-optimizing both the parameter of merging SPCA bands and LDA bands, and the classification accuracy. Our extensive experiments on two real hyperspectral datasets (AVIRIS 1992 Indian pine image and HYDICE image of Washington DC Mall), proves that the proposed technique is not only robust but offers more classification accuracy than many conventional dimension reduction techniques over several well known classifiers.

关键词： Hyperspectral data fusion image region classification remote sensing spectral band compression aviris

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Classification of Hyperspectral Data Using a Few Training Samples

Classification of Hyperspectral Data Using a Few Training Sa...

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The 2010 IEEE International Conference on Progress in Informatics and computing

作者： Qazi Sami ul Haq Key Laboratory of Pervasive Computing Ministry of Education Department of Computer Science and TechnologyTsinghua UniversityBeijing 100084China

Few training samples are a major concern in many data classification *** remote sensing and especially in hyperspectral imaging,the problem is even more serious because of extreme difficulty and cost factor involved in the labeling of *** hyperspectral data classification,more training samples are required for good classification results due to the availability of large number of *** this paper,we propose a novel approach to tackle the problem of few samples for the purpose of hyperspectral data *** the approach, an ensemble framework is proposed which is based on sparse *** framework consists of individual sparse based *** representation is calculated for each test pixel using l\ -*** vote technique is then used for the prediction of class labels of test *** test samples which have been agreed upon by the individual sparse classifiers and which have minimum residuals for any particular class,are then added to the train pool for the subsequent *** in this way the problem of lack of train samples is *** chose the sparse based classification because it is relatively new in remote sensing and does not need dimension reduction,any learning procedure or any model selection for parameters,still its performance is better than the state-of-the-art *** tested the proposed framework using the well known AVIRIS 1992 Indiana's Indian Pines hyperspectral *** comparison with the state of the art methods prove the efficiency of the proposed framework.

关键词： Index Terms-Hyperspectral data sparse image region classification remote sensing spectral band compression aviris

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Macroblock level hybrid temporal-spatial prediction for H.264/AVC

Macroblock level hybrid temporal-spatial prediction for H.26...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Mou Xiao Pin Tao Jianwen Chen Wenting Wu Jiangtao Wen Key Laboratory of Pervasive Computing Ministry of Education Tsinghua National Laboratory of Information Science and Technology Department of Computer Science Tsinghua University Beijing China

In this paper, a novel macroblock level hybrid temporal-spatial video coding framework is proposed. In this framework, a new Hybrid Temporal Spatial Prediction (HTSP) coding mode is adopted for each macroblock. Macroblock is divided into two partitions. The first partition is temporally predicted using motion compensation and encoded, while the second partition is spatially predicted using the reconstruction of the first partition. Experimental results show that up to 0.4 dB coding gain and 0.2 dB on average can be achieved for HD sequences.

关键词： Automatic voltage control Video coding Motion compensation High definition video Redundancy Spatial resolution Laboratories Decoding Encoding Transform coding

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Detection of behavioral contextual properties in asynchronous pervasive computing environments

Detection of behavioral contextual properties in asynchronou...

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16th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2010

作者： Huang, Yu Yu, Jianping Cao, Jiannong Tao, Xianping State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Department of Computer Science and Technology Nanjing University Nanjing 210093 China Internet and Mobile Computing Lab. Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong

ISBN: (纸本)9780769543079

Detection of contextual properties is one of the primary approaches to enabling context-awareness. In order to adapt to temporal evolution of the pervasive computing environment, context-aware applications often need to detect behavioral properties specified over the contexts. This problem is challenging mainly due to the intrinsic asynchrony of pervasive computing environments. However, existing schemes implicitly assume the availability of a global clock or synchronous coordination, thus not working in asynchronous environments. We argue that in pervasive computing environments, the concept of time needs to be reexamined. Toward this objective, we propose the Ordering Global Activity (OGA) algorithm, which detects behavioral contextual properties in asynchronous environments. The essence of our approach is to utilize the message causality and its on-the-fly coding as logical vector clocks. The OGA algorithm is implemented and evaluated based on the open-source context-aware middleware MIPA. The evaluation results show the impact of asynchrony on the detection of contextual properties, which justifies the primary motivation of our work. They also show that OGA can achieve accurate detection of contextual properties in dynamic pervasive computing environments. © 2010 IEEE.

关键词： Middleware

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Middleware support for context-awareness in asynchronous pervasive computing environments

Middleware support for context-awareness in asynchronous per...

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International Workshop on Wireless Network Algorithm and Theory

作者： Yu, Jianping Huang, Yu Cao, Jiannong Tao, Xianping State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Department of Computer Science and Technology Nanjing University Nanjing 210093 China Internet and Mobile Computing Lab. Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong

ISBN: (纸本)9780769543222

Context-awareness is an essential feature of pervasive applications, and runtime detection of contextual properties is one of the primary approaches to enabling context-awareness. However, existing context-aware middleware does not provide sufficient support for detection of contextual properties in asynchronous environments. We argue that in asynchronous environments, the concept of time needs to be reexamined. Instead of assuming the availability of global time or synchronous interaction, we should rely on logical time. To this end, we present the Middleware Infrastructure for Predicate detection in Asynchronous environments (MIPA), which supports context-awareness based on logical time. Design and operation of MIPA are explained in detail. We also evaluate MIPA with a comprehensive case study. The evaluation results show the cost-effectiveness and scalability of MIPA. © 2010 IEEE.

关键词： Cost effectiveness

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Modeling Optimal Organization of the Internet-Based Computation in the Cloud computing Environment

Modeling Optimal Organization of the Internet-Based Computat...

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2010 IEEE Global Telecommunications Conference GLOBECOM 2010

作者： Ji Lu Yaoxue Zhang Yuezhi Zhou Key Laboratory of Pervasive Computing Ministry of Education Tsinghua National Laboratory of Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

It is regarded that the Internet-based computing has a lot of significant merits. However, the relatively long latency and relatively high cost of wide-area networking hamper the growth of its application. Currently, there are some computing nodes emerging in the Internet, such as the public data centers born in the initial Cloud computing environment, which can perform computations with large computational freedom and capability for the public use. This paper proposes and models the problem of optimal organization of the Internet-based computation which takes these emerging distributed computing nodes into account. Both the performance and cost are analytically formulized into the optimization problems. Because the solution space expands exponentially when directly addressing the problems, this paper develops two efficient approaches. Extensive evaluations on various networks and workloads show that the proposed model OIC outperforms the current Internet-based computing model in terms of both computational time and cost.

关键词： Peer to peer computing Computational modeling Internet Receivers Servers Complexity theory Optimization

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Optimally organizing distributed-component computing in the clouds: From both the user perspective and resource view

Optimally organizing distributed-component computing in the ...

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International Conference on Electronics And Information Engineering (ICEIE)

作者： Ji Lu Yaoxue Zhang Yuezhi Zhou Key Laboratory of Pervasive Computing Ministry of Education Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

With the sharp increase of computing demands, computations more and more rely on the distributed components. However, the relatively inferior quality of wide-area networks hampers such trend. This paper proposes an approach aiming at optimally organizing the distributed-component computing in the current Internet in which some nodes with large computational freedom and capability for the public to compute are emerging, such as the initially born data centers in the present Cloud computing environments. Both the user experience and the resource utilization are taken into the quantitative analysis and optimization. Facing the exponential growth of the problem's solution space, this paper develops an effective approach. Experimental simulations show that the approach generates the result significantly approximating the optimal solution, in the meantime with high efficiency.

关键词： Peer to peer computing Optimization Distributed databases Receivers Processor scheduling Cloud computing

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International Symposium on I/V Communications and mobile Network (ISVC)

作者： A. Kribach M. Boulmalf H. Harroud Wireless and Mobile Computing Laboratory Computer Science Department School of Science and Engineering Al Akhawayn University Morocco

Radio frequency identification (RFID) technology has emerged to facilitate tracking items and inexpensively automate the processes of supply chain management. The deployment of RFID technology requires the development of a robust middleware which will manage the huge amount of data captured by RFID antennas and sent by RFID readers. The main objective of this article is to provide insights into how radio frequency identification (RFID) can enhance supply chain management (SCM) applications. Our approach is based on developing a generic interface on top of the FlexRFID middleware in order to allow existing SCM applications get the benefit from RFID technology. Our Wireless and mobile computing lab (WML) has initiated the development of the FlexRFID middleware since fall 2008. Since then, the work of our lab puts the light on the emergence of RFID technology in the market. The results of this work indicate that our approach will enhance many processes of SCM through the advantages of RFID technology starting from inventory control, asset management, work in process, and ending at tracking the shipments. On the whole, the generic interface that we have developed on top of the FlexRFID middleware will allow companies lower not only the cost of the deployment of RFID technology, but also it will help companies reuse their existing SCM applications and thus maintaining the usability of their applications.

关键词： Radiofrequency identification Middleware Companies Supply chain management Supply chains XML Real time systems

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