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Automated Aerial Image Analysis usi
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Contents ii Page Certificate of Aut
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List of Figures iv Page Figure 1: A
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Abstract This study sets out an alg
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The process was designed so that it
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• Something capable of handling i
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of a control to calibrate most of t
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1.2 General Introduction and Backgr
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the higher the chances of successfu
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overlaid aerial imagery. This is du
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upper case, beginning with lower ca
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a selected study area. This is poss
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2 Stepping through the Algorithm Th
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The process can also be coded into
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Aerial imagery is a form of databas
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The software required for this step
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study area from the photography and
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The areas extracted were placed int
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ands was detected the standard devi
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2.4 Confirmation The final stage of
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3 Sampling for the Baseline Image K
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Figure 3: Road area and vector data
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(The previous samples were compared
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Road test sample 2 Mean pixel value
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3.2 Water Figure 4: Typical Water A
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The purpose of this thesis is to id
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present in the target area (or its
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possible to quickly process each se
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Marsh Sample 1 Mean Pixel Value Sta
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Marsh test Sample 1 (Pasture) Mean
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form both pasture and road/ paving
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Forest Sample 1 Mean pixel value St
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Coniferous forestry test sample 1 (
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whole was not analyzed but individu
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infrared signatures to identify the
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sampled in this study. In relation
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3.6 Track Figure 9: Typical Track A
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surface area that might have been i
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(Phynn et al, 2002). In this study
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3.7 Shade Figure 10: Typical Shade
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- Page 83 and 84: trends of bordering polygons mentio
- Page 85 and 86: 3.8 Roof Areas Figure 12: Typical R
- Page 87 and 88: Figure 13: Distribution of Building
- Page 89 and 90: These mean greyscale pixel values f
- Page 91 and 92: Roof test sample 1 Mean pixel value
- Page 93 and 94: 3.9 Pasture Figure 15: Typical Past
- Page 95 and 96: From the above samples, samples 4 a
- Page 97 and 98: For the track/ hard cover the diffe
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- Page 101 and 102: The identification of rough pasture
- Page 103 and 104: cycle being suggested here to prese
- Page 105 and 106: 4 Testing This chapter describes a
- Page 107 and 108: Figure 17: Creating the ASCII file
- Page 109 and 110: Figure 20: Green colour band for pa
- Page 111 and 112: Figure 23: Green colour band for pa
- Page 113 and 114: The peak for the green colour band,
- Page 115 and 116: Figure 30: Green colour band for pa
- Page 117 and 118: Figure 31: Vector data for rough pa
- Page 119 and 120: Figure 34: Green colour band for ro
- Page 121 and 122: Figure 37: Green colour band for ro
- Page 123 and 124: The peak for the green colour band,
- Page 125 and 126: Figure 44: Green colour band for ro
- Page 127 and 128: The first sample area came from a p
- Page 129 and 130: Figure 47: Red colour band for mars
- Page 131: Figure 50: Aerial view of marsh tes
- Page 135 and 136: Figure 55: Red colour band for mars
- Page 137 and 138: Figure 57: Aerial view of marsh tes
- Page 139 and 140: 4.4 Bog Test The sampling for areas
- Page 141 and 142: Figure 62: Red colour band for bog
- Page 143 and 144: Figure 65: Vector data for bog test
- Page 145 and 146: The histogram for the green colour
- Page 147 and 148: Figure 70: Aerial view for bog test
- Page 149 and 150: This trend was continued for the bl
- Page 151 and 152: The green colour band pixel count a
- Page 153 and 154: 4.5 Conclusion Image segmentation i
- Page 155 and 156: 5 Literature Review The goal of thi
- Page 157 and 158: shown give a more detailed picture
- Page 159 and 160: dependencies” (Kettling, P.330).
- Page 161 and 162: identifying coffee plantations from
- Page 163 and 164: apply an algorithm to colour the da
- Page 165 and 166: In 2002 S. Phinn, M. Stanford, P. S
- Page 167 and 168: with the authors work for a softwar
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- Page 171 and 172: 16*16 pixels as urban or nonurban.
- Page 173 and 174: as roads and car parks are so simil
- Page 175 and 176: H. van der Werff and F. van der Mee
- Page 177: Shen, S. S., Badhwar, G. D., and Ca