Enhancement Classification of Galaxy Images
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See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/275963346 Enhancement Classification of Galaxy Images Thesis · December 2014 DOI: 10.13140/RG.2.1.4152.4641 CITATIONS READS 0 493 1 author: John Jenkinson University of Texas at San Antonio 5 PUBLICATIONS 53 CITATIONS SEE PROFILE All content following this page was uploaded by John Jenkinson on 07 May 2015. The user has requested enhancement of the downloaded file. ENHANCEMENT CLASSIFICATION OF GALAXY IMAGES APPROVED BY SUPERVISING COMMITTEE: Arytom Grigoryan, Ph.D., Chair Walter Richardson, Ph.D. David Akopian, Ph.D. Accepted: Dean, Graduate School Copyright 2014 John Jenkinson All rights reserved. DEDICATION To my family. ENHANCEMENT CLASSIFICATION OF GALAXY IMAGES by JOHN JENKINSON, M.S. DISSERTATION Presented to the Graduate Faculty of The University of Texas at San Antonio In Partial Fulfillment Of the Requirements For the Degree of MASTER OF SCIENCE IN ELECTRICAL ENGINEERING THE UNIVERSITY OF TEXAS AT SAN ANTONIO College of Engineering Department of Electrical and Computer Engineering December 2014 UMI Number: 1572687 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. UMI 1572687 Published by ProQuest LLC (2015). Copyright in the Dissertation held by the Author. Microform Edition © ProQuest LLC. All rights reserved. This work is protected against unauthorized copying under Title 17, United States Code ProQuest LLC. 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, MI 48106 - 1346 ACKNOWLEDGEMENTS My most sincere regard is given to Dr. Artyom Grigoryan for giving me the opportunity to learn to research and for being here for the students, to Dr. Walter Richardson, Jr. for teaching complex topics from the ground up and leading this horse of a student to mathematical waters applicable to my research, to Dr. Mihail Tanase for being the study group that I have never had, and to Dr. Azima Mottaghi for constant motivation, support and the remark, "You can finish it all in one day." Additionally, this work was progressed through discussions with Mehdi Hajinoroozi, Skei, hftf, and pavonia. I also acknowledge the UTSA Mexico Center for their support of this research. December 2014 iv ENHANCEMENT CLASSIFICATION OF GALAXY IMAGES John Jenkinson, B.S. The University of Texas at San Antonio, 2014 Supervising Professor: Arytom Grigoryan, Ph.D., Chair With the advent of astronomical imaging technology developments, and the increased capacity of digital storage, the production of photographic atlases of the night sky have begun to generate volumes of data which need to be processed autonomously. As part of the Tonantzintla Digi- tal Sky Survey construction, the present work involves software development for the digital image processing of astronomical images, in particular operations that preface feature extraction and clas- sification. Recognition of galaxies in these images is the primary objective of the present work. Many galaxy images have poor resolution or contain faint galaxy features, resulting in the mis- classification of galaxies. An enhancement of these images by the method of the Heap transform is proposed, and experimental results are provided which demonstrate the image enhancement to improve the presence of faint galaxy features thereby improving classification accuracy. The fea- ture extraction was performed using morphological features that have been widely used in previous automated galaxy investigations. Principal component analysis was applied to the original and en- hanced data sets for a performance comparison between the original and reduced features spaces. Classification was performed by the Support Vector Machine learning algorithm. v TABLE OF CONTENTS Acknowledgements...................................... iv Abstract............................................. v ListofTables..........................................viii ListofFigures......................................... ix Chapter1:Introduction.................................... 1 1.1GalaxyClassification................................. 1 1.1.1 Hubble Scheme . 2 1.1.2 deVaucouleursScheme........................... 7 1.2 Digital Data Volumes in Modern Astronomy . 12 1.2.1 DigitizedSkySurveys............................ 12 1.2.2 ProblemMotivation............................. 14 1.3 Problem Description and Proposed Solution . 14 1.4PreviousWork..................................... 15 1.4.1 Survey of Automated Galaxy Classification . 15 1.4.2 Survey of Support Vector Machines . 17 1.4.3 Survey of Enhancement Methods . 18 Chapter 2: Morphological Classification and Image Analysis . 20 2.1 Astronomical Data Collection . 20 2.2Imageenhancementmeasure(EME)......................... 22 2.3Spatialdomainimageenhancement......................... 25 2.3.1 NegativeImage................................ 27 2.3.2 LogarithmicTransformation......................... 28 vi 2.3.3 PowerLawTransformation.......................... 30 2.3.4 HistogramEqualization........................... 31 2.3.5 MedianFilter................................. 34 2.4Transform-basedimageenhancement........................ 37 2.4.1 Transforms.................................. 37 2.4.2 Enhancement methods . 44 2.5ImagePreprocessing................................. 47 2.5.1 Segmentation................................. 48 2.5.2 Rotation,ShiftingandResizing....................... 53 2.5.3 CannyEdgeDetection............................ 58 2.6DataMiningandClassification............................ 61 2.6.1 FeatureExtraction.............................. 61 2.6.2 Principal Component Analysis . 64 2.6.3 Support Vector Machines . 67 2.7ResultsandDiscussion................................ 73 2.8FutureWork...................................... 82 AppendixA:ProjectSoftware................................ 84 A.1PreprocessingandFeatureExtractioncodes..................... 85 A.2SVMClassificationcodeswithdata......................... 92 A.2.1Originaldata................................. 92 A.2.2Enhanceddata................................100 Bibliography..........................................110 Vita vii LIST OF TABLES Table 1.1 Hubble’s Original Classification of Nebulae Table . 3 Table 2.1 Morphological Feature Descriptions . 64 Table2.2 FeatureValuesPerClass........................... 64 Table 2.3 Galaxy list and relation between NED classification and current project classification................................. 74 Table 2.4 Summary of classification results for original and enhanced data. Accuracy improved by 12.924% due to enhancement. 81 viii LIST OF FIGURES Figure 1.1 Hubble Tuning Fork Diagram. Image from http://www.physast.uga.edu/ rl- s/astro1020/ch20/ch26_fig26_9.jpg. 2 Figure 1.2 Plate scan of Elliptical and Irregular Nebulae from Mount Wilson Obser- vatory originally included in Hubble’s paper, Extra-galactic Nebulae. 4 Figure 1.3 Plate scan of Spiral and Barred Spiral Nebulae from Mount Wilson Obser- vatory originally included in Hubble’s paper, Extra-galactic Nebulae. 6 Figure 1.4 A plane projection of the revised classification scheme. 10 Figure 1.5 A 3-Dimensional representation of the revised classification volume and notationsystem................................. 11 Figure 1.6 Sloan Digital Sky Survey coverage map. http://www.sdss.org/sdss-surveys/. ........................................ 13 Figure 2.1 Schmidt Camera of Tonantzintla. Permission to use image from the Insti- tuto Nacional de Astrofísica, Óptica y Electrónica (INAOE). 20 Figure 2.2 Plate Sky Coverage. Permission to use image from the Instituto Nacional deAstrofísica,ÓpticayElectrónica(INAOE)................. 21 Figure 2.3 Digitized plate AC8431 . 23 Figure 2.4 Marked plate scan AC8431 . 24 Figure 2.5 Plate scan AC8409 . 25 Figure 2.6 Marked plate scan AC8409 . 26 Figure 2.7 Cropped galaxies from plate scans AC8431 and AC8409 read left to right and top to bottom: NGC 4251, 4274, 4278, 4283, 4308, 4310, 4314, 4393, 4414, 4448, 4559, 3985, 4085, 4088, 4096, 4100, 4144, 4157, 4217, 4232, 4218, 4220, 4346, 4258. 27 Figure 2.8 Negative, log and power transformations. 28 ix Figure 2.9 Top to bottom: Galaxy NGC4258 and its Negative Image. 29 Figure 2.10 Logarithmic and nthroottransformations................... 30 Figure 2.11 γ-powertransformation............................ 31 Figure 2.12 Galaxy NGC 4217 power law transformations. 32 Figure 2.13 Histogram processing to enhance Galaxy NGC 6070. 34 Figure 2.14 Top to Bottom: Histogram of original and enhanced image. 35 Figure 2.15 Illustration of the median of a set of points in different dimensions. 36 Figure 2.16 Signal-flow graph of determination of the five-point transformation by a vector x =(x0,x1,x2,x3,x4) . ........................ 43 Figure 2.17 Network of the x-induced DsiHT of the signal z................ 44 Figure 2.18 Intensity values and spectral coefficients of Galaxy NGC 4242. 46 Figure 2.19 Butterworth lowpass filtering performed in the Fourier (frequency) domain. 47 Figure 2.20 α-rooting enhancement of Galaxy NGC 4242................. 47 Figure 2.21 Top: Galaxy PIA 14402, Bottom: NGC 5194, both processed by Heap transform.................................... 48 Figure 2.22 Computational scheme for galaxyclassification................ 49 Figure 2.23 Background subtraction of Galaxy NGC 4274 by manual and