Imagery Geospatial Conflation
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AUTOMATED VECTOR-VECTOR AND VECTOR- IMAGERY GEOSPATIAL CONFLATION ________________________________________________________________________ A Dissertation Presented to The Faculty of the Graduate School University of Missouri-Columbia _____________________________________________________________ In Partial Fulfillment Of the Requirements for the Degree Doctor of Philosophy _____________________________________________________________ by WENBO SONG Dr. James M. Keller, Dissertation Supervisor MAY 2011 The undersigned, appointed by the dean of the Graduate School, have examined the dissertation entitled AUTOMATED VECTOR-VECTOR AND VECTOR-IMAGERY GEOSPATIAL CONFLATION Presented by Wenbo Song, A candidate for the degree of Doctor of philosophy And hereby certify that, in their opinion, it is worthy of acceptance. Professor James Keller Professor Marjorie Skubic Professor Curt Davis Professor Chi-Ren Shyu Professor Kannappan Palaniappan ACKNOWLEDGMENTS First, I would like to express my deepest gratitude to my advisor, Dr. James Keller for his intellectual and invaluable guidance during my doctoral study. It was my pleasure and honor to be his student. I would not have finished this work without his training and direction. Also I would like to thank my doctoral committee members, Dr. Marjorie Skubic, Dr. Curt Davis, Dr. Chi-Ren Shyu and Dr. Kannappan Palaniappan for their insightful suggestions on my dissertation. Special thanks to my boss Tim Haithcoat at MU Geographic Resources Center. This dissertation would not have been possible without his support. Also I would like to thank my colleagues at MU Geographic Resources Center for their help. Finally, I would like to thank my family, my wife Jie Ning, my son David and daughter Jenna. They are always there to support and encourage me. ii TABLE OF CONTENTS ACKNOWLEDGEMENTS……………………………………………………………..ii LIST OF FIGURES………..………...……………………………………....................vii LIST OF TABLES………………………………………………………………………xi ABSTRACT…………………………………………………………………………….xii CHAPTER 1. INTRODUCTION……………………………………………………………….1 1.1 Map……………………………………………………….…………………..1 1.2 Classic Topographic Mapmaking…………………………………………….2 1.3 Digital Mapping Revolution……...…………………………………………..8 1.4 Remote Sensing Imagery…………………………...……………………….11 1.5 Need for Conflation………………………………………...……………….21 2. A SNAKE BASED APPROACH FOR TIGER ROAD DATA CONFLATION…………………………………………………………………33 2.1 Introduction………………………………………………..………………..34 2.2 Traditional Conflation Methodology………………………………………..36 2.2.1 Feature Matching…………………………………………………….37 2.2.2 Map Alignment………………………………………………………39 2.2.3 Attributes Transfer…………………………………………………...40 2.3 Snake Based Conflation Approach…………………………..……………..41 iii 2.3.1 Feature Matching……………………………………………….……43 2.3.2 Map Alignment………………………………………………………45 2.3.3 Position Correction Using Snakes…………………………………...45 2.4 Experiment Results...…………………………………..…………………...48 2.5 Discussions and Conclusion.…………………………………..…………...53 3. RELAXATION-BASED POINT FEATURE MATCHING FOR VECTOR MAP CONFLATION…………………………………………………………..56 3.1 Introduction………………………………………..……………………….57 3.2 Previous Works on Point Feature Matching………………………..………60 3.3 Methodology………………………..………………………………………65 3.4 Experiments………………………………..………………………………..74 3.5 Summary of Point Feature Matching..…………………..………………….83 4. AUTOMATED GEOSPATIAL CONFLATION OF VECTOR ROAD MAPS TO HIGH RESOLUTION IMAGERY……………………………………….84 4.1 Introduction………………………………………………..………………..85 4.2 Methodology…………………………………………………..……………94 4.2.1 Spatial Contextual Information Extraction…………………………..95 4.2.2 Road Intersection and Road Termination Extraction………………...98 4.2.3 Point Matching by Relaxation Labeling……………………………101 4.2.4 Piecewise Local Affine Transformation (Rubber-Sheeting)……….107 4.2.5 Snake-based Position Correction…………………………………...108 iv 4.2.6 Refinement………………………………………………………….112 4.3 Experiments and Accuracy Assessment…………………….………..……113 4.4 Discussions and Conclusion.……………………………..……………......117 5. SCALE-UP EXPERIMENT OF VECTOR-TO-IMAGERY CONFLATION ……………………………………………………………….120 5.1 The Need for Scale-up Experiment………………………………………..120 5.2 Use Classified Road Imagery to speed up the Process…………………….124 5.3 Accuracy Assessment……………………………………………………...133 5.4 Edge Matching…………………………………………………………….144 5.5 Performance issues.………………………………………………………..150 5.6 Additional Snake Experiment……………………………………………..154 5.7 Experiment in downtown area……………………………………………..156 6. IMPROVING THE POSITIONAL ACCURACY OF DIGITAL PARCEL MAP THROUGH VECTOR-TO-IMAGERY CONFLATION……………161 6.1 Introduction………………………………………………..………………162 6.2 Parcel Mapping Methods and Transformation Techniques…………….....163 6.2.1 Parcel Mapping Methods.………………………………………….163 6.2.2 Transformation Techniques………………………………………...165 6.3 Proposed Vector-to-Imagery Conflation Methodology……………..…….166 6.3.1 Generate Vector Road Centerline from Parcel Map……….….........167 6.3.2 Extract Road Intersections from Vector Road Centerline………….167 v 6.3.3 Extract Road Intersections from Imagery…………………………..168 6.3.4 Point Feature Matching …………………………………………….168 6.3.5 Piecewise Transformation…………………………………………..168 6.4 Experiment Results……………………………..…………………………169 6.5 Discussions and Conclusion………………………………..……………..177 7. CONCLUSION AND FUTURE WORK…………………………………….179 7.1 Summary of completed Research………………………………………….179 7.1.1 Vector-to-vector conflation…………………………………………180 7.1.2 Point Feature Matching by Relaxation Labeling………………...…181 7.1.3 Vector-to-imagery Conflation………………………………………183 7.1.4 Vector-to-imagery Scale-up Experiment…………………………...184 7.1.5 Parcel Map Migration through Vector-to-imagery Conflation……..185 7.2 Future Work……………………………………………………………….186 7.3 List of Publications………………………………………………………...188 REFERNCE LIST……………………………………………………….…………....189 APPENDIX A. Scale-up Experiment Results……………………..……………………….199 B. SNAKE Experiment Results……………………..………………………..253 VITA…………………………………………………………………………………...271 vi LIST OF FIGURES Figure Page 1.1 Plane-table surveying by USGS topographers.…..………………………………..3 1.2 Overlapping aerial photographs provide stereoscopic coverage of areas to be Mapped..…………………………………………………………………………..4 1.3 Portion of 1:24,000 USGS topographic map of Columbia, MO.…………………7 1.4 Currentness of revised maps compared to original maps…..……………………..8 1.5 Vector digitizing with a tablet and raster scanning with a drum scanner…………9 1.6 Portion of Census 2000 county block map of Columbia, MO……..……….……11 1.7 Landsat 7 ETM+ multispectral 30 m resolution imagery of Columbia, MO……13 1.8 Multispectral 4 m resolution IKONOS imagery of Columbia, MO………….….14 1.9 Black & white 1 m resolution USGS digital orthophoto quadrangle (DOQ) of MU Campus, MO……………………………….……………………………….....…17 1.10 Multispectral 1 m resolution NAIP imagery of memorial stadium, Columbia, MO ………………………………………………………………………………19 1.11 2ft resolution multispectral Imagery of memorial stadium, Columbia, MO..…...20 1.12 Multispectral 6 inch resolution imagery of memorial stadium, Columbia, MO...21 2.1 The difference matching scenarios………………………………………………41 2.2 Feature matching by geometric and topology criteria…………………………...44 2.3 Illustration of the movement of snake…………………………………………...48 2.4 One section of the test area………………………………………………………51 2.5 Another section the test area……………………………………………………..52 vii 2.6 Vector-Imagery conflation for section of the test area…..………………………54 3.1 TIGER roads and MODOT roads are superimposed on an aerial photograph…..59 3.2 The example is used to illustrate the relaxation process…….………………..….71 3.3 TIGER roads and MODOT roads of 9 rural test areas in Columbia…...…….….75 3.4 TIGER roads and MODOT roads of another 9 test areas in Columbia………….76 3.5 The matching result produced by proximity algorithm for one rural area……….78 3.6 Comparison of the matching results for one portion of suburban area…..………80 4.1 The TIGER vector roads (red) are superimposed on an aerial photograph……...86 4.2 The work flow of vector-to-imagery conflation process……………………..….95 4.3 Comparison of original TIGER roads (green) and final conflation result (red) for test area shown in Fig 4.1.... ………………………………………………….. 115 4.4 Comparison of original TIGER roads (green) and final conflation result (red) for a rural area….…………………………………………………………………...115 5.1 Selected test tiles used in table 4.1…….………………………………………..121 5.2 Two big scale-up test areas in Columbia, MO….………………………………122 5.3 TIGER roads (blue) of nine test sections in rural area of Columbia…..……….123 5.4 TIGER roads (blue) of nine test sections in suburban area of Columbia………124 5.5 Color infrared image of one suburban test area………………………………...126 5.6 The classified road/building regions for figure 5.5……………………………..127 5.7 The grayscale image of length measure………………………………………...128 viii 5.8 Result of bigger threshold……………………………………………………...129 5.9 Result of smaller threshold……………………………………………………..130 5.10 The classified results…………………………………………………………....131 5.11 The final result after removing smaller regions………………………………...132 5.12 The road image is generated by thresholding the length measure……………...133 5.13 The TIGER Roads are overlaid on top of MODOT road buffer…………….....136 5.14 The TIGER roads are overlaid on imagery……………………………………..137 5.15 TIGER roads are overlaid with MODOT road buffer zone…………………….138 5.16 The conflated TIGER roads are overlaid on MODOT buffer zone…………….139 5.17 The conflated TIGER roads are overlaid on road image……………………….140 5.18 The conflated roads in rural areas before edge matching………………………146 5.19 The generated buffer zones along the edges of adjoin