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Road value sample 3 Mean pixel value Red 202.3 Green 228.8 Blue 183.9 Road value sample 4 Mean pixel value Red 163.083 Green 189.75 Blue 147.667 Road value sample 5 Mean pixel value Red 171.417 Green 196.083 Blue 153.333 Road value sample 6 Mean pixel value Red 169.444 Green 190.111 Blue 151.444 Road value sample 7 Mean pixel value Red 167.111 Green 190.444 Blue 155.889 Road value sample 8 Mean pixel value Red 155.667 Green 185.25 Blue 145.833 Road value sample 9 Mean pixel value Red 148.417 Green 172.25 Blue 143.833 Road value sample 10 Mean pixel value Red 187.061 Green 211.545 Blue 173.364 Table 1: Road sample values 37
(The previous samples were compared to statistics from the image as a whole of Red: Grey Level Values: 6 – 255, Median: 115, Mean: 111.711, StdDev:27.2804; Green: Grey Level Values: 30 – 255, Median: 135, Mean: 136.542, StdDev:27.3776; Blue: Grey Level Values: 5 – 255, Median: 102, Mean: 102.636, StdDev:17.8523) The mean pixel values are a useful benchmark to base further analysis of the imagery on and the fact that each sample displayed a more or less uniform deviation from the image standard implies that there is some merit to applying the results to a key which identifies impermeable surface area. In general such areas in an urban area will contain similar properties to a road surface. Further iterations of this sampling will involve sampling shingle against concrete and tar (see the analysis of the spectral values contained in areas of track) in order to see if there is a measurable spectral variation between them; this will, however, involve a small amount of post processing to enhance the differences between them. The road network is a useful point of reference in automated image analysis and ways of analyzing imagery to capture road networks have been well studied (such as pattern analysis techniques explored by van der Werff & van der Meer, 2008). In this thesis the focus is not on capturing road data, something which has been completed and is on a continuous revision cycle, but on utilizing this data to make the image analysis process easier. Most study areas (and almost every part of the island of Ireland) will have at least some part of the road network present (at the risk of sounding pedantic this assumption is not verified here because it can be assumed as general knowledge and can be discerned from a cursory look at any online small scale representation of the network). This means that there are polygons of a specific unique spectral value available for referencing most studies, even when confined to a particular area or series of photographs. In order to get a clearer insight into how this can be applied three areas close to roads around the image were sample and compared to sample values from their closest road polygon. 38
Page 1 and 2: Automated Aerial Image Analysis usi
Page 3 and 4: Contents ii Page Certificate of Aut
Page 5 and 6: List of Figures iv Page Figure 1: A
Page 7 and 8: Abstract This study sets out an alg
Page 9 and 10: The process was designed so that it
Page 11 and 12: • Something capable of handling i
Page 13 and 14: of a control to calibrate most of t
Page 15 and 16: 1.2 General Introduction and Backgr
Page 17 and 18: the higher the chances of successfu
Page 19 and 20: overlaid aerial imagery. This is du
Page 21 and 22: upper case, beginning with lower ca
Page 23 and 24: a selected study area. This is poss
Page 25 and 26: 2 Stepping through the Algorithm Th
Page 27 and 28: The process can also be coded into
Page 29 and 30: Aerial imagery is a form of databas
Page 31 and 32: The software required for this step
Page 33 and 34: study area from the photography and
Page 35 and 36: The areas extracted were placed int
Page 37 and 38: ands was detected the standard devi
Page 39 and 40: 2.4 Confirmation The final stage of
Page 41 and 42: 3 Sampling for the Baseline Image K
Page 43: Figure 3: Road area and vector data
Page 47 and 48: Road test sample 2 Mean pixel value
Page 49 and 50: 3.2 Water Figure 4: Typical Water A
Page 51 and 52: The purpose of this thesis is to id
Page 53 and 54: present in the target area (or its
Page 55 and 56: possible to quickly process each se
Page 57 and 58: Marsh Sample 1 Mean Pixel Value Sta
Page 59 and 60: Marsh test Sample 1 (Pasture) Mean
Page 61 and 62: form both pasture and road/ paving
Page 63 and 64: Forest Sample 1 Mean pixel value St
Page 65 and 66: Coniferous forestry test sample 1 (
Page 67 and 68: whole was not analyzed but individu
Page 69 and 70: infrared signatures to identify the
Page 71 and 72: sampled in this study. In relation
Page 73 and 74: 3.6 Track Figure 9: Typical Track A
Page 75 and 76: surface area that might have been i
Page 77 and 78: (Phynn et al, 2002). In this study
Page 79 and 80: 3.7 Shade Figure 10: Typical Shade
Page 81 and 82: currently available for all areas a
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
Page 99 and 100:
3.10 Rough Pasture Figure 16: Typic
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:
Page 113 and 114:
The peak for the green colour band,
Page 115 and 116:
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:
Page 123 and 124:
The peak for the green colour band,
Page 125 and 126:
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 and 132:
Figure 50: Aerial view of marsh tes
Page 133 and 134:
from which any variance would flag
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
Page 169 and 170:
ased analysis, solely spectral base
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
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