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and comparing the remainder to expected spectral values for hard cover could return this value. The results from the six samples are in the table below: Track sample 1 Mean Pixel Value Standard Deviation Red 178.5 14.02 Green 194 15.231 Blue 146.5 19 Track sample 2 Mean Pixel Value Standard Deviation Red 164 23.013 Green 193.833 14.729 Blue 147.5 15.149 Track sample 3 Mean Pixel Value Standard Deviation Red 196.75 10.511 Green 219.875 12.240 Blue 145 16.639 Track sample 4 Mean Pixel Value Standard Deviation Red 193.8 193.8 Green 207.067 207.067 Blue 153.667 153.667 Track sample 5 Mean Pixel Value Standard Deviation Red 190.833 15.014 Green 208.667 11.089 Blue 148.5 10.518 Track sample 6 Mean Pixel Value Standard Deviation Red 190.5 2.738 Green 217.167 7.574 Blue 138 5.44 Table 13: Track sample values While the above samples outline unique pixel signatures when compared to the entire image (40% above the mean for red, 35% for green and 30% for the blue colour bands) the difference between these samples and those returned for the standard road network is not significant. This does not necessarily make it difficult to distinguish between standard tarred road and other types of impermeable 67
surface area that might have been introduced to the landscape. The road network is present in the vector dataset so subtracting this (along with the other known polygons such as water, buildings etc.) from the means that polygons with a high number of pixels corresponding to these values would indicate that the surface contains an area of cover similar to track or road (hard impermeable cover). This unique value has potential to increase the accuracy of flood mapping and prediction but requires additional processing to ensure that the high values are the result of permeable surface area. This could take place within an automated software process by swapping the colour bands to increase the difference between these areas and areas of vegetation. The purpose for sampling the areas of track was to see if there could be any means of determining if an area surrounding a private dwelling had been paved, or if there were any paved yards/ areas of hard cover present in other semi urban polygons. The test sampling took three areas to compare the values for track against; an unpaved dirt track, an area of compacted gravel yard and an area of paved yard. With the exception of the blue colour band the results were similar to those from cut pasture. These can be discerned from cut pasture by setting the search algorithm to look at the mean pixel value for the blue colour band, which was 30% less for both the paved yard and dirt track. The values returned for the yard of compacted dirt and gravel were similar to cut pasture but were within a small polygon containing several roofed buildings. The algorithm can therefore be set to accept values similar to cut pasture for small area polygons containing a number of roofed buildings as gravel/ dirt hard cover. In the particular case of this sample the results obtained are most likely due to pigment in the gravel biasing the sample. 68
Page 1 and 2:
Automated Aerial Image Analysis usi
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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
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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
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 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: 3.6 Track Figure 9: Typical Track A
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 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 121 and 122: Figure 37: Green colour band for ro
Page 123 and 124: The peak for the green colour band,
Page 125 and 126:
Figure 44: Green colour band for ro
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
Page 135 and 136:
Figure 55: Red colour band for mars
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Figure 57: Aerial view of marsh tes
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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
Page 147 and 148:
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
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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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