An Assessment of the SRTM Topographic Products - Jet Propulsion ...

Recommendations

Info

LIST OF FIGURES 6 5.1 Range offset versus time delay for all GCPs in each of Beams 1-4 . . . . . . . . . . . 85 5.2 Distribution of range offsets for beams 1–4. Counts are are given per 0.5 m bin. . . . 86 5.3 Range offsets for beams 1-4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 5.4 Range offset versus time delay and the residual of a linear fit. . . . . . . . . . . . . . 90 5.5 Kinematic GPS track over mountainous terrain . . . . . . . . . . . . . . . . . . . . . 91 5.6 Surface plot of the standard deviation of the height error sampled along a kinematic GPS track over a mountainous SRTM cell . . . . . . . . . . . . . . . . . . . . . . . . 92 5.7 Histogram of the SRTM descending image brightness sampled along a kinematic GPS track with all data and with rejected points removed . . . . . . . . . . . . . . . . . . 93 5.8 Kinematic GPS track used in Figure 5.9. There are 3862 points. . . . . . . . . . . . 93 5.9 Surface plot of the measured mean brightness along the kinematic GPS track for a single SRTM cell in the Australian outback . . . . . . . . . . . . . . . . . . . . . . . 94 5.10 Best fit geolocation biases in ascending image data using 10 ◦ by 10 ◦ super-cells in North America. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.11 Best fit geolocation biases in descending image data using 10 ◦ by 10 ◦ super-cells in North America. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.12 Best fit geolocation biases in DEM data using 10 ◦ by 10 ◦ super-cells in North America. 98 5.13 Best fit geolocation biases using combined height and image data for 10 ◦ by 10 ◦ super-cells in North America . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 5.14 Best fit geolocation biases in ascending image data using 10 ◦ by 10 ◦ super-cells in South America. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.15 Best fit geolocation biases in descending image data using 10 ◦ by 10 ◦ super-cells in South America. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.16 Best fit geolocation biases in DEM data using 10 ◦ by 10 ◦ super-cells in South America.100 5.17 Best fit geolocation biases using combined height and image data for 10 ◦ by 10 ◦ super-cells in South America . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 5.18 Best fit geolocation biases in ascending image data using 10 ◦ by 10 ◦ super-cells in Africa. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 5.19 Best fit geolocation biases in descending image data using 10 ◦ by 10 ◦ super-cells in Africa. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 5.20 Best fit geolocation biases in DEM data using 10 ◦ by 10 ◦ super-cells in Africa. . . . 102 5.21 Best fit geolocation biases using combined height and image data for 10 ◦ by 10 ◦ super-cells in Africa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 5.22 Best fit geolocation biases in ascending image data using 10 ◦ by 10 ◦ super-cells in New Zealand. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 5.23 Best fit geolocation biases in descending image data using 10 ◦ by 10 ◦ super-cells in New Zealand. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 5.24 Best fit geolocation biases in DEM data using 10 ◦ by 10 ◦ super-cells in New Zealand. 104 5.25 Best fit geolocation biases using combined height and image data for 10 ◦ by 10 ◦ super-cells in New Zealand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.26 Best fit geolocation biases in ascending image data using 10 ◦ by 10 ◦ super-cells in Eurasia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 5.27 Best fit geolocation biases in descending image data using 10 ◦ by 10 ◦ super-cells in Eurasia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 5.28 Best fit geolocation biases in DEM data using 10 ◦ by 10 ◦ super-cells in Eurasia. . . . 106
LIST OF FIGURES 7 5.29 Best fit geolocation biases using combined height and image data for 10 ◦ by 10 ◦ super-cells in Eurasia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 5.30 Best fit geolocation biases in ascending image data using 10 ◦ by 10 ◦ super-cells in Australia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.31 Best fit geolocation biases in descending image data using 10 ◦ by 10 ◦ super-cells in Australia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.32 Best fit geolocation biases in DEM data using 10 ◦ by 10 ◦ super-cells in Australia. . . 108 5.33 Best fit geolocation biases using combined height and image data for 10 ◦ by 10 ◦ super-cells in Australia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 6.1 Overview image of GeoSAR data from Santa Barbara County, California with a single color wrap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 6.2 Overview image of GeoSAR data from Santa Barbara County, California with a single colors repeating every 100 m of elevation . . . . . . . . . . . . . . . . . . . . . . . . . 112 6.3 Histogram of pixel by pixel height differences between SRTM and GeoSAR derived DEMs in Santa Barbara County, California . . . . . . . . . . . . . . . . . . . . . . . 113 6.4 Five views of the same small area of the scene including X-Band GeoSAR backscatter amplitude, shaded relief of the GeoSAR DEM, SRTM heights, GeoSAR heights, and he SRTM-GeoSAR height difference . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 6.5 Five views of the same small area of the scene including X-Band GeoSAR heights, SRTM heights, shaded relief of the GeoSAR heights, shaded relief of the SRTM heights, and the GeoSAR amplitude . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 6.6 Four views of the same small area of the scene including X-Band GeoSAR backscatter amplitude, shaded relief of the GeoSAR Dem, the GeoSAR heights (color wrap), and the SRTM-GeoSAR height difference . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 6.7 Line cuts through the SRTM and GeoSAR DEMs . . . . . . . . . . . . . . . . . . . . 118 6.8 Histogram of pixel by pixel height differences between SRTM and GeoSAR derived DEMs in Santa Barbara County, California . . . . . . . . . . . . . . . . . . . . . . . 119 6.9 W . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 6.10 Wrapped height differences between the SRTM and GeoSAR derived DEMs in Santa Barbara County, California with the GeoSAR data having been filtered using a 2x2 box car filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 6.11 Wrapped height differences between the SRTM and GeoSAR derived DEMs in Santa Barbara County, California with the GeoSAR data having been filtered using a 3x3 box car filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 6.12 Wrapped height differences between the SRTM and GeoSAR derived DEMs in Santa Barbara County, California with the GeoSAR data having been filtered using a 4x4 box car filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 A.1 SRTM Measurement Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 A.2 IFSAR measurement geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 A.3 Local Coordinate Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 A.4 Action of the Projection Operator . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 A.5 Rotation about the axis ˆz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
Page 1 and 2: An Assessment of the SRTM Topograph
Page 3 and 4: CONTENTS 3 4 Random Errors and the
Page 5: LIST OF FIGURES 5 2.20 South Americ
Page 9 and 10: Summary The NASA/NIMA (now NGA) Shu
Page 11 and 12: CHAPTER 1. OVERVIEW 11 1.1 SRTM Per
Page 13 and 14: CHAPTER 1. OVERVIEW 13 1.2.2 DTED L
Page 15 and 16: CHAPTER 1. OVERVIEW 15 filtering an
Page 17 and 18: CHAPTER 1. OVERVIEW 17 Figure 1.3:
Page 19 and 20: CHAPTER 1. OVERVIEW 19 Latitude 60N
Page 21 and 22: CHAPTER 1. OVERVIEW 21 (δh(x) −
Page 23 and 24: CHAPTER 1. OVERVIEW 23 the error mo
Page 25 and 26: Chapter 2 Absolute Height Error In
Page 27 and 28: CHAPTER 2. ABSOLUTE HEIGHT ERROR 27
Page 57 and 58:
CHAPTER 3. SPATIAL STRUCTURE OF THE
Page 59 and 60:
Page 61 and 62:
Page 63 and 64:
Page 65 and 66:
Page 67 and 68:
Page 69 and 70:
Page 71 and 72:
Page 73 and 74:
CHAPTER 4. RANDOM ERRORS AND THE TE
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Page 81 and 82:
Page 83 and 84:
Chapter 5 Geolocation Error In cont
Page 85 and 86:
CHAPTER 5. GEOLOCATION ERROR 85 Fig
Page 87 and 88:
CHAPTER 5. GEOLOCATION ERROR 87 Tab
Page 89 and 90:
CHAPTER 5. GEOLOCATION ERROR 89 to
Page 91 and 92:
Page 93 and 94:
Page 95 and 96:
CHAPTER 5. GEOLOCATION ERROR 95 Con
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
CHAPTER 5. GEOLOCATION ERROR 101 Fi
Page 103 and 104:
Page 105 and 106:
Page 107 and 108:
Page 109 and 110:
Chapter 6 Comparison Against GeoSAR
Page 111 and 112:
CHAPTER 6. COMPARISON AGAINST GEOSA
Page 113 and 114:
Page 115 and 116:
Page 117 and 118:
Page 119 and 120:
Page 121 and 122:
Page 123 and 124:
Page 125 and 126:
Bibliography [1] J. van Zyl, “The
Page 127 and 128:
APPENDIX A. SRTM ERROR CHARACTERIST
Page 129 and 130:
Page 131 and 132:
Page 133 and 134:
Page 135 and 136:
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143:
show all

An Assessment of the SRTM Topographic Products - Jet Propulsion ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?