高光譜影像異常物偵測與識別之平行運算方法與其效能評估

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姓名

楊任傑(Ren-Jie Yang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

高光譜影像異常物偵測與識別之平行運算方法與其效能評估
(Parallel Computing of Anomaly Detection and Discrimination for Hyperspectral Imagery: a Performance Evaluation)

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摘要(中)

本研究致力於發展一套平行運算方法，針對高光譜影像做異常物
偵測與相異物質區別；透過個人電腦叢集 (PC cluster) 環境運算，達到有效提升偵測速率的目的。
我們的偵測方法主要概念，如果將影像中的背景視為高斯分佈 (Gaussian distribution)，由於異常物 (anomalies) 或人造物的光譜特性和背景有著極大差異且面積相對背景來說較小，因此可藉由找尋和背景分佈的不同來達到異常物偵測目的。若資料是一個高斯分佈，代入偏度 (skewness) 和峰度 (kurtosis) 的式子後結果應為零，如果資料非高斯分佈，利用偏度與峰度便可偵測有無異常物的存在。
結合偏度和峰度運算與正交子空間投影 (Orthogonal Subspace Projection, OSP) 概念，可達到偵測多類異常物之目的；不過受限於運算流程，提供一個初始投影行向量在一個時間內只能找出一類異常物，欲找出其他異常物，則需等待前一個異常物被找出後才能再進行。然而給定不同的投影行向量將可偵測出不同的異常物，因此本研究提出平行化的運算方法，透過訊息傳遞介面 (Message-Passing Interface, MPI) 的函式庫在個人電腦叢集上進行平行運算，將一個初始投影行向量傳送給一個CPU，讓一個CPU找出一類異常物，使具有多個CPU的叢集電腦在同一時間內找出多類異常物，大大提高偵測效率。
實驗利用美國太空總署噴射推進實驗室 (NASA’s Jet Propulsion Laboratory) 所發展的AVIRIS (Airborne Visible / InfraRed Imaging Spectrometer) 高光譜影像做測試，評估此平行化異常物偵測與識別演算法的運算效能。在兩套分別擁有四個節點 (4 Nodes) 但硬體性能相異的個人電腦叢集環境上執行平行演算法，並觀察比較不同運算環境下的效能差異。相較於在單台個人電腦上的運算時間，四節點叢集電腦可達到平均約2.7倍的速度提升，且保有原循序作法相同的物質偵測結果。

摘要(英)

The aim of this thesis is to develop a parallel version of the high-order statistic algorithm for anomalies detection and discrimination in hyperspectral images. This approach was implemented by C language with the message passing interface (MPI) library on PC cluster.
Anomaly detection is to detect unknown small targets from an unknown background. As a result, anomaly detection in an unknown image scene can be accomplished by searching the deviation from background distribution. It is known that skewness and kurtosis are the normalized third and fourth central moments which can be used to measure the asymmetry and flatness of the data distribution respectively. Since Gaussian distribution has zero skewness and kurtosis, if the distribution of the image has high skewness or kurtosis, it cannot be modeled as Gaussian and there are some anomalies resident in the image scene. Therefore anomaly can be detected by finding the maximum skewness or kurtosis direction.
Using skewness or kurtosis with orthogonal subspace projection, anomaly discrimination can be achieved. Limited by operation procedure, a projector can only detect one anomaly at a time and the next procedure has to wait until the previous one had finished. Hence we propose a parallel version of the high-order statistic algorithm to distribute the computation loading. In our approach, each node will process one randomly initialized projector, and it will detect one anomaly target when the projector had converged. By means of this measure, the PC cluster is able to search for more than one anomaly at the same time. The algorithm then collects and analyzes the projectors from all nodes, and redistributes them to all nodes to search for the next possible anomalies.
The computational performance of the proposed parallel anomaly detection algorithm has been evaluated by the AVIRIS (Airborne Visible/InfraRed Imaging Spectrometer) data provided by NASA’’s Jet Propulsion Laboratory. A 2.7 times speed up in average can be achieved with four nodes PC cluster, and the detection results is also the same as the serial method running on single CPU.

關鍵字(中)

★ 峰度
★ 偏度
★ 高光譜影像
★ 高次統計方法
★ 異常物偵測
★ 個人電腦叢集
★ 平行運算

關鍵字(英)

★ kurtosis
★ skewness
★ hyperspectral imagery
★ parallel computing
★ PC cluster
★ anomaly detection
★ high-order statistics

論文目次

Abstract i
Contents iii
List of Figures v
List of Tables vii
List of Symbols viii
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Overview of the study 2
1.2.1 Anomaly detection using skewness or kurtosis 2
1.2.2 Parallel algorithm for anomaly detection 3
1.3 Thesis organization 4
Chapter 2 Literature Review 6
2.1 Parallel programming 6
2.1.1 Parallel computer architectures 7
2.1.2 Message passing interface 9
2.1.3 Performance evaluation 10
2.2 AVIRIS hyperspectral images 13
2.3 Anomaly detection 15
2.4 Parallel computing 17
Chapter 3 Anomaly Detection Using Skewness or Kurtosis 20
3.1 Sphering 21
3.2 Projection searching process 23
Chapter 4 Parallel Algorithm for Anomaly Detection 27
4.1 Procedure decomposition 27
4.2 Data decomposition 30
Chapter 5 Experiments and Evaluations 32
5.1 Experimental environment 32
5.2 Experimental data 34
5.3 Performance evaluation 35
5.3.1 Influence of node number 35
5.3.2 Influence of data size 40
5.3.3 Influence of network transmission 45
5.4 Anomaly detection and image classification results 49
5.5 Discussions 55
Chapter 6 Conclusions and Future Works 57
6.1 Conclusions 57
6.2 Future works 58
Bibliography 59
Appendix A Experimental Result 63
Appendix B MPI Functions 84
Appendix C Description of AVIRIS Sensor System 91

參考文獻

[1] An overview of hyperspectral remote sensing, CSIRO Earth Observation Centre, http://www.eoc.csiro.au/hswww/Overview.htm.
[2] AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) homepage, http://aviris.jpl.nasa.gov/.
[3] Barry, W. and A. Michael, Parallel Programming – Techniques and applications Using Networked Workstations and Parallel Computers, Prentice Hall, 1999.
[4] Chang, C.-I. and S.-S. Chiang, “Anomaly Detection and Classification for Hyperspectral Imagery,” IEEE Trans. on Geoscience and Remote Sensing, vol.40, no.6, pp. 1314-1325, June 2002.
[5] Chang, S.-C., A Parallel Loop Self-Scheduling for Heterogeneous PC Clusters, M.S. dissertation, Dept. of Computer Science and Information Engineering, Tunghai Univ., Taichung, 2003.
[6] Chang, Y.-L., J.-P. Fang, H. Ren and W.-Y. Liang, “A Parallel Computing Technique for Complete Modular Eigenspace Feature Extraction of Hyperspectral Images,” in Proc. IGARSS 2006, pp. 960-963, Aug. 2006.
[7] Cuprite, Nevada Research Papers, U.S. Geological Survey, http://speclab.cr.usgs.gov/cuprite.html.
[8] Embedded and Parallel Systems Laboratory, Department of CSIE, NTUT, http://xms.eps.csie.ntut.edu.tw/labweb.
[9] Garcia, V., E. Debreuve and M. Barlaud, “Fast k Nearest Neighbor Search using GPU,” in Proc. CVPR Workshops 2008, pp. 1-6, June 2008.
[10] Harsanyi, J. C. and C.-I. Chang, “Hyperspectral Image Classification and Dimensionality Reduction: An Orthogonal Subspace Projection Approach,” IEEE Trans. on Geoscience and Remote Sensing, vol. 32, no. 4, pp. 779-785, July 1994.
[11] High-performance Linux clustering, Part 1: Clustering fundamentals, IBM, http://www.ibm.com/developerworks/linux/library/l-cluster1/.
[12] Hyperspectral imaging, Wikipedia,
http://en.wikipedia.org/wiki/Hyperspectral_imaging.
[13] Ian, T., Designing and Building Parallel Programs, Addison Wesley, 1995.
[14] Ke, Y.-C., Ocean Anomaly Detection Using Optical Satellite Images, M.S. dissertation, Civil Eng. Dept., Univ. of Central, Taoyuan County, 2008.
[15] Kurtosis, Wikipedia, http://en.wikipedia.org/wiki/Kurtosis.
[16] Message Passing Interface, Blaise Barney, Lawrence Livermore National Laboratory, http://www.llnl.gov/computing/tutorials/mpi/.
[17] Michael, J. Q., Parallel Programming in C with MPI and OpenMP, McGraw Hill, 2003.
[18] Moore’s Law, Wikipedia, http://en.wikipedia.org/wiki/Moore's_law.
[19] M. P. I. Forum, “A message passing interface standard,” [Online],
http://www.mpi-forum.org/index.html.
[20] NVIDIA CUDA Programming Guide version 2.0, June 2008.
[21] Okuyama, T., F. Ino and K. Hagihara, “A Task Parallel Algorithm for Computing the Costs of All-Pairs Shortest Paths on the CUDA-compatible GPU,” in Proc. ISPA '08, pp. 284-291, Dec. 2006.
[22] Pattern Recognition and Electronic Design Automation Laboratory, Department of EE, NTUT, http://140.124.43.96/.
[23] Peter, S. P., Parallel Programming with MPI, Morgan Kaufmann, 1997.
[24] Plaza, A., D. Valencia and C.-I. Chang, “Parallel Implementation of Endmember Extraction Algorithms From Hyperspectral Data,” IEEE Geoscience and Remote Sensing Letters, vol. 3, no. 3, pp. 334-338, July 2006.
[25] Reed, I. S. and X. Yu, “Adaptive Multiple-Band CFAR Detection of an Optical Pattern with Unknown Spectral Distribution,” IEEE Trans. on Acoustic, Speech and Signal Processing, vol. 38, no. 10, pp. 1760-1770, Oct. 1990.
[26] Ren, H., Q. Du, J. Wang, C.-I. Chang, J. O. Jensen and J. L. Jensen, “Automatic Target Recognition for Hyperspectral Imagery using High-Order Statistics,” IEEE Trans. on Aerospace and Electronic Systems, vol. 42, no. 4, pp. 1372-1383, Oct. 2006.
[27] Ren, H., Q. Du and J. O. Jensen, “Efficient Anomaly Detection and Discrimination for Hyperspectral Image,” in Proc. SPIE AeroSence 2002, Orlando, Florida, vol.4725, pp. 234-241, April 2002.
[28] Schweizer, S. M., J. M. F. Moura, “Efficient detection in hyperspectral imagery,” IEEE Trans. on Image Processing, vol. 10, no. 4, pp. 584-597, April 2001.
[29] Skewness, Wikipedia, http://en.wikipedia.org/wiki/Skewness.
[30] Valencia, D., A. Lastovetsky and A. Plaza, “Design and Implementation of a Parallel Heterogeneous Algorithm for Hyperspectral Image Analysis Using HeteroMPI,” in Proc. ISPDC '06, pp. 301-308, July 2006.
[31] Wang, C.-C., Theoretical Analysis of Parallel Data Decomposition on Cluster Grid, M.S. dissertation, Dept. of Computer Science and Information Engineering, Chung-Hua Univ., Hsin-Chu, 2007.
[32] William H, Numerical recipes in C++: the art of scientific computing, Cambridge University Press, 2002.

指導教授

任玄(Hsuan Ren)

審核日期

2009-7-7

推文