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Rough Pasture Sample 1 Mean pixel value Standard Deviation Red 81.61 31.475 Green 120.334 31.922 Blue 94.606 17.356 Rough Pasture Sample 1 Mean pixel value Standard Deviation Red 73.432 34.752 Green 114.44 36.594 Blue 95.898 18.988 Rough Pasture Sample 1 Mean pixel value Standard Deviation Red 69.694 35.687 Green 111.165 37.813 Blue 95.857 18.771 Rough Pasture Sample 1 Mean pixel value Standard Deviation Red 66.43 27.096 Green 105.61 28.348 Blue 94.081 17.243 Table 25: Rough pasture sample values As mentioned above; it could be possible to obtain similar values to rough pasture in a spectral analysis of mixed forestry or certain urban developments. This, however, does not present a problem for the potential image key as both those areas can be eliminated from an automatic survey due to the fact that both mixed forestry and areas containing buildings and dwellings can be identified by previously captured attributes. In terms of the spectral values for the image as a whole the red band was 35% lower, the green 17% lower and blue 7% lower across the samples. While this alone does not give complete indication of rough pasture, when coupled with high levels of standard deviation in the red and green colour bands the probability of this type of land cover is high. When coupled with an additional vetting process (eliminating known land cover types), it should be possible to identify rough pasture using lower red and green colour band values with high standard deviation. 93
The identification of rough pasture using an automated process would also lend itself to the study of abandoned developments. It could reasonable be assumed that the presence of rough pasture within more than half the plots of land in a development indicates that it has been abandoned. This might allow for a quick survey of developments to determine the level of repair by feeding the associated polygon set into a process designed to identify the percentage of rough pasture. In particular levels of pixel values in the red colour band similar to those above would indicate that the site has been abandoned. On an anecdotal level the rate at which former construction sites can be reclaimed by vegetation has been impressive over the last few years of good growing weather. Although there are several other ways to identify incomplete dwellings such as missing access roadways and ordnance survey field revision text the presence of spectral values corresponding to rough pasture would indicate long term neglect. Because of the relatively high level of standard deviation associated with the sampling for rough pasture it is probably one of the more important parts of automated image processing to identify as once these areas are removed from the search remaining areas displaying similar levels of deviation in the red and green colour bands can be analyzed more closely (possibly even with flagged histograms for users to identify). In order to recognise the values discovered relative to the rest of the image three separate areas of cover were sample. 94
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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
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: 3.10 Rough Pasture Figure 16: Typic
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 113 and 114: The peak for the green colour band,
Page 117 and 118: Figure 31: Vector data for rough pa
Page 119 and 120: Figure 34: Green colour band for ro
Page 123 and 124: The peak for the green colour band,
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
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The green colour band pixel count a
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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).
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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
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H. van der Werff and F. van der Mee
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Shen, S. S., Badhwar, G. D., and Ca
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