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Pasture test sample 1 (track) Mean pixel value Standard deviation Red 213.952 18.444 Green 236.762 16.532 Blue 194.833 19 Pasture test sample 2 (coniferous forestry) Mean pixel value Standard deviation Red 72.566 19.204 Green 111.643 21.468 Blue 96.381 16.026 Pasture test sample 3 (mixed forestry) Mean pixel value Standard deviation Red 69.968 32.486 Green 104.287 32.054 Blue 86.217 19.492 Table 24: Pasture test sample values The above samples were chosen from areas outside those already captured for the baseline survey of the spectral values for track, coniferous and mixed forestry. The track was chosen as hard cover gives unique high values and is of benefit in calibrating a proportional difference between target areas (such as pasture in this case) and its high values. As was noted above, pasture itself is also useful in calibrating a key for an algorithm due to its specific mean colour values in the red colour band and low level of standard deviation from those mean values, but is not coded in the original vector input data and can only be derived from the image processing. The area of mixed forestry was chosen for its high level of standard deviation and the fact that it has similar spectral values to rough pasture. Mixed forestry will be identified and present in most imagery (or the specific properties could be pre-set into a key for the automated analysis) so using those values would allow an automated processing technique to eliminate areas with this level of high standard deviation from the process. 89
For the track/ hard cover the difference in the values for mean number of pixels converted to greyscale for the red colour band was significantly higher. This was also the case for the mean of the green colour band where the variation was upwards of 40% for the pasture which had not been cut. It should be noted, however, that the mean pixel in the green colour band was higher for cut pasture and this variation brought it closer to the value of hard ground –the difference in the mean of the blue colour band between the test sample and all the samples of pasture (including the cut pasture samples) was consistently 40% higher. This differential (in conjunction with a low standard deviation and a mean 10% to 40% lower than hard ground in the red and green colour bands) could be used to determine the presence of pasture. The two forestry samples showed a mean pixel value in the red colour band of approximately 50% less than pasture –with an increased standard deviation for both. In terms of distinguishing between coniferous and mixed; the standard deviation was one third higher for the red and green colour bands in the mixed forestry. The mean value for the green colour band in both of the forestry types was 40% lower than the value of the mean in the pasture, giving another relative indicator to calibrate pasture values from. Small areas of forestry are found close to most semi urban areas and the fact that they are coded and measured means they are useful in training an algorithm to match land use to neighbouring polygons. The aim of this sampling is to try and achieve a number of probability factors that will allow an automated process to determine land use or coverage types based on the available data. With this in mind a broad variety of samples were taken and the relative proportions between them assessed. In the above example the pasture area was compared against other known area types. The increased amount of cross checking allows the elimination of areas which conform to a particular type. In this way the image is gradually broken down until every polygon is referenced. The value of this is not necessarily in the ability to classify field use (although this has some merit in that it adds value to available mapping) but in the identified process. This enables a piece of software to be developed which can be reused 90
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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
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 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: From the above samples, samples 4 a
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 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
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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
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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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