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Spectral Textile Detection in the VNIR/SWIR Band James A Air Force Institute of Technology AFIT Scholar Theses and Dissertations Student Graduate Works 3-26-2015 Spectral Textile Detection in the VNIR/SWIR Band James A. Arneal Follow this and additional works at: https://scholar.afit.edu/etd Recommended Citation Arneal, James A., "Spectral Textile Detection in the VNIR/SWIR Band" (2015). Theses and Dissertations. 21. https://scholar.afit.edu/etd/21 This Thesis is brought to you for free and open access by the Student Graduate Works at AFIT Scholar. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of AFIT Scholar. For more information, please contact [email protected]. SPECTRAL TEXTILE DETECTION IN THE VNIR/SWIR BAND THESIS James A. Arneal, Second Lieutenant, USAF AFIT-ENG-MS-15-M-049 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY AIR FORCE INSTITUTE OF TECHNOLOGY Wright-Patterson Air Force Base, Ohio DISTRIBUTION STATEMENT A APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. The views expressed in this document are those of the author and do not reflect the official policy or position of the United States Air Force, the United States Department of Defense or the United States Government. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. AFIT-ENG-MS-15-M-049 SPECTRAL TEXTILE DETECTION IN THE VNIR/SWIR BAND THESIS Presented to the Faculty Department of Electrical and Computer Engineering Graduate School of Engineering and Management Air Force Institute of Technology Air University Air Education and Training Command in Partial Fulfillment of the Requirements for the Degree of Master of Science in Electrical Engineering James A. Arneal, B.S.E.E Second Lieutenant, USAF MARCH 2015 DISTRIBUTION STATEMENT A APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. AFIT-ENG-MS-15-M-049 SPECTRAL TEXTILE DETECTION IN THE VNIR/SWIR BAND THESIS James A. Arneal, B.S.E.E Second Lieutenant, USAF Committee Membership: Lt Col Jeffrey D. Clark, PhD Chair Richard K. Martin, PhD Member Gilbert L. Peterson, PhD Member AFIT-ENG-MS-15-M-049 Abstract Dismount detection, the detection of persons on the ground and outside of a vehicle, has applications in search and rescue, security, and surveillance. Spatial dismount detection methods lose effectiveness at long ranges, and spectral dismount detection currently relies on detecting skin pixels. In scenarios where skin is not exposed, spectral textile detection is a more effective means of detecting dismounts. This thesis demonstrates the effectiveness of spectral textile detectors on both real and simulated hyperspectral remotely sensed data. Feature selection methods determine sets of wavebands relevant to spectral textile detection. Classifiers are trained on hyperspectral contact data with the selected wavebands, and classifier parameters are optimized to improve performance on a training set. Classifiers with optimized parameters are used to classify contact data with artificially added noise and remotely-sensed hyperspectral data. The performance of optimized classifiers on hyperspectral data is measured with Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The best performances on the contact data are 0.892 and 0.872 for Multilayer Per- ceptrons (MLPs) and Support Vector Machines (SVMs), respectively. The best per- formances on the remotely-sensed data are AUC = 0.947 and AUC = 0.970 for MLPs and SVMs, respectively. The difference in classifier performance between the contact and remotely-sensed data is due to the greater variety of textiles represented in the contact data. Spectral textile detection is more reliable in scenarios with a small variety of textiles. iv Acknowledgements I thank my advisor, Lt Col Jeffrey Clark, for his help at every stage of this thesis' development. His commitment to my work was the best, last, and only line of defense against thesis deadlines. Have a fantastic retirement, Sir. Many thanks to my committee members, Dr. Richard Martin and Dr. Gilbert Peterson, for giving me the feedback I needed to make my thesis the best it could be. To my fellow Sensors Exploitation Research Group (SERG) lab inhabitants, thanks for keeping me sane. Shane Fernandes and Stephen Sweetnich, your support on the technical aspects of remote sensing proved invaluable. And to Capt Khoa Tang: it has been an honor suffering through it all with you. Thanks to my family for their support through nineteen straight years of school. It hasn't been easy, and I couldn't have done it without you. James A. Arneal v Table of Contents Page Abstract . iv Acknowledgements . .v List of Figures . viii List of Tables . xiii I. Introduction . .1 1.1 Problem Statement . .1 1.2 Justification . .3 1.3 Assumptions . .4 1.4 Standards . .5 1.5 Approach . .5 1.6 Materials and Equipment . .6 II. Background . .7 2.1 Hyperspectral Imaging for Dismount Detection . .7 2.2 Methods of Feature Selection . .8 Genetic Algorithms . .9 Local Search Methods . 11 Information Theory Methods. 11 Bhattacharyya Methods . 15 Principal Component Analysis . 15 Support Vector Machine - Recursive Feature Elimination . 16 Relief/Relief-F . 17 2.3 Techniques for Detection or Classification . 17 Spectral Matching . 17 Spectral Matched Filter . 18 Support Vector Machines . 19 Bayesian Classifiers . 21 Multilayer Perceptrons . 21 2.4 Spectral Properties of Textiles . 23 Composition . 24 Environment . 25 III. Methodology . 27 3.1 Data Sets . 27 Contact Data Collection . 29 Remotely-Sensed Data Collection . 30 vi Page 3.2 Contact Data Pre-Processing. 31 3.3 Noise Addition . 34 3.4 Classification Algorithms . 36 3.5 Feature Selection . 39 FCBF Implementation . 39 SFS Implementation . 39 Generation of Varied Feature Sets . 41 3.6 Classifier Optimization . 43 3.7 Remotely-Sensed Data Pre-Processing . 46 3.8 Analysis of Optimized Classifiers . 48 IV. Results . 50 4.1 Data Collection . 50 Contact Data Collection . 50 Remotely-Sensed Data Collection . 52 4.2 Data Pre-Processing . 53 Contact Data Pre-Processing. 53 Remotely-Sensed Data Pre-Processing . 56 4.3 Feature Selection . 57 4.4 Classifier Optimization . 59 V. Conclusions and Future Work . 73 5.1 Summary of Methodology and Results . 73 5.2 Future Work . 74 Appendix A. List of Materials in Training/Testing and Generalization Sets . 79 Appendix B. Additional Multi-Layer Perceptron (MLP) Receiver Operating Characteristic (ROC) curves . 81 Appendix C. Additional Support Vector Machine (SVM) ROC curves .................................................... 89 Appendix D. Structures, Weights, and Biases of Selected Classifiers . 94 4.1 Highest-rated Image MLP . 94 4.2 Highest-rated Generalization MLP . 96 4.3 Highest-rated Image SVM . 98 4.4 Highest-rated Generalization SVM . ..
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