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Road test sample 1 Mean pixel value Standard deviation Red 205.889 8.18 Green 228.167 11.289 Blue 178.778 12.73 Adjacent spectral values sample 1 (pasture) Mean pixel value Standard deviation Red 91.539 6.573 Green 138.706 7.504 Blue 92.519 10.84 Table 2: Road test sample value 1 The first test sample took an area of road and a sample area from an area of pasture adjacent to the road. This can be assumed to be a recurring set of values that can be located in most aerial imagery that this study is considering. In the test the values for standard deviation from the mean across all three colour bands in both samples did not vary to any large extent and can be omitted as reference values for the search algorithm looking to match a set of values as pasture using the nearest road polygon as key. In contrast there was a large difference in the mean values for all three colour bands with the road polygon containing pixels of a mean 50% higher for the red, 40% higher for the green and 50% higher for the blue colour band. This discernable difference allows a range surrounding this relative difference to be included into the search and areas of pasture to be identified. Note: potential candidates for a pasture set of polygons are also cross checked against other relative values, outlined in later sections of this sampling study part of the thesis. A result of this variance means that once the known areas are identified the road set can be compared against unknown polygons adjacent to it and, given similar levels of standard deviation and proportional mean by colour band outlined above, the unknown polygons can be placed in a pasture polygon set for further reference and confirmation as the analysis progresses. 39
Road test sample 2 Mean pixel value Standard deviation Red 157.961 7.537 Green 181.211 8.08 Blue 145.553 10.129 Adjacent test values sample 2 (bog) Mean pixel value Standard deviation Red 111.479 7.086 Green 125.162 8.764 Blue 98.326 12.152 Table 3: Road test sample value 2 This sample looked at an area of bog adjacent to a road for discernable pixel variations between both, although bog will be sampled further at another stage in this thesis it is worth noting that all bogs have access roads nearby and a spectral comparison is a useful reference. The surface of the roads mentioned varies but, as is outlined in the road section of this thesis, surface type has only small affect on the range of spectral values returned from the road. The standard deviation for all three colour bands was almost identical between both samples (road and bog) which could be expected due to the relative uniformity of surface cover (from a medium altitude aerial perspective). The mean pixel value for these colour bands, however, varied almost uniformly across the three bands with a 30% smaller value obtained for the red, green and blue bands in the bog sample. As with road and pasture this is a strong proportional variation for analysis purposes and allows bog to be established in an initial polygon set during processing. The set can then be compared against the other expected variances (water, forestry, pasture) and matched against polygon size (bog will almost always the largest area polygon in any sample –other large polygons such as lakes and forestry are coded and can be automatically placed in a set during analysis). 40
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 and 44: Figure 3: Road area and vector data
Page 45: (The previous samples were compared
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
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from which any variance would flag
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Figure 55: Red colour band for mars
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Figure 57: Aerial view of marsh tes
Page 139 and 140:
4.4 Bog Test The sampling for areas
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Figure 62: Red colour band for bog
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Figure 65: Vector data for bog test
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The histogram for the green colour
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Figure 70: Aerial view for bog test
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This trend was continued for the bl
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The green colour band pixel count a
Page 153 and 154:
4.5 Conclusion Image segmentation i
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5 Literature Review The goal of thi
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shown give a more detailed picture
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dependencies” (Kettling, P.330).
Page 161 and 162:
identifying coffee plantations from
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apply an algorithm to colour the da
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In 2002 S. Phinn, M. Stanford, P. S
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with the authors work for a softwar
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ased analysis, solely spectral base
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16*16 pixels as urban or nonurban.
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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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