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samples were taken at three separate sections around the image; they did not correspond to samples taken for other parts of this study (specific road, building, pasture areas etc.) so as to increase the variety of input data. The three areas consisted of forestry (coniferous plantation), pasture and track. The pasture sample also contained a high degree of shade, which was not rectified in the table to see if it could be possible to identify this type of area with shade included. Water values testing sample 1 (forestry) 45 Mean Pixel Value Standard Deviation Red 73.532 19.591 Green 112.507 21.605 Blue 96.772 16.042 Water values testing sample 2 (pasture) Mean Pixel Value Standard Deviation Red 115.153 12.691 Green 167.608 15.487 Blue 104.872 13.2 Water values testing sample 3 (track) Mean Pixel Value Standard Deviation Red 213.429 10.748 Green 237.429 12.369 Blue 192.821 14.636 Table 6: Water test sample values As might be expected the track (artificial surface) showed the greatest difference, with a red colour band value of less than 20% of that found in track. The relative disparity between these two values (the red mean pixel value in areas of water and track/ road) could be used to calibrate an image key during automatic image analysis; and the percentages of other less distinct land cover derived by comparison. One of the main aims of this study is to see if it would be possible to analyze aerial imagery using an automatic process based on vector data. With known water polygons and road polygons present this can be achieved, however, as was mentioned above the body of water needs to be large enough to obtain an accurate baseline reading for the photography run. If only drains or streams are
present in the target area (or its immediate (~1km) surroundings then extracting a set of baseline pixel values from water polygons would not benefit the analysis. It can therefore be concluded that water polygons provide a useful reference for image analysis, but in the context of using them to add value to ordnance survey small area polygons they need to be part of areas not less than five pixels in diameter (i.e. belong to classes of rivers, lakes or large ponds). When the control values for water (from first table) were compared to pasture the red and green colour bands showed values which could be used to calculate if pasture was present in a polygon. The mean pixel value for water in the red colour band was 30% of the value for pasture and only 45% of the value for the green colour band found in the pasture sample (which included a section of shade). The value for the blue colour band also had a disparity of just over 20% less than the pasture mean pixel value. This implies that if an algorithm was to be run on sections of ordnance survey data which took polygon co-ordinates from the vector polygons, calibrated a key from the water polygon and compared the red and green band colour values against the red and green colour values of the neighbouring polygon and then confirmed the level of standard deviation (which was found to be low in an area of pasture, ~10 on the greyscale) it is probable the area can be labelled as pasture. In itself this does not present much of a breakthrough but when added to the known polygon it helps complete the picture of a target area being analyzed. At this point it might be better to think of the image as its vector representation. As the polygons which the vector data encloses are identified the areas can be filled. In this way the study is filling the blanks around known values. If the result was thought of as a mosaic of known area properties (type and nature of land cover) then applying the label pasture dramatically reduces the areas left to identify. 46
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 and 46: (The previous samples were compared
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: The purpose of this thesis is to id
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
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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
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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
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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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