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for the clue colour band, as with the previous three test polygons, remained consistent with an expected range for marsh (see sampling section): Figure 59: Blue colour band for marsh test 4 The values returned for the test of marsh polygons were not as distinct as the previous three polygon tests (pasture, rough pasture and bog). There were sets of values (such as a consistent blue range and gradual increase in count from shade to the expected marsh range) which can be used to classify this type of area, but the classification needs to be confined to the latter (step 3) part of an automatic search algorithm to increase the probability of the spectral values matching correctly. 131
4.4 Bog Test The sampling for areas of bog took larger polygons which, with forestry and water bodies already eliminated from the search algorithm (with the vector data) could automatically be assumed to have a high probability of belonging to an area of bog. The first sample was taken from an area of bog bounded by roads on all sides in the northeast of the imagery being used in this study (see the general introduction for details). This polygon was chosen for sampling as it could be considered a good example of this type of land cover: Figure 60: Vector data for bog test 1 The data points which form the polylines bounding the polygon were exported in ASCII format. As with all clip files for this algorithm the points are recorded in 132
Page 1 and 2:
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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currently available for all areas a
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trends of bordering polygons mentio
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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 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: Figure 57: Aerial view of marsh tes
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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