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Pasture Sample 2 Mean Pixel Value Standard Deviation Red 139.318 6.267 Green 182.748 8.016 Blue 115.605 12.547 Pasture Sample 3 Mean Pixel Value Standard Deviation Red 133.267 8.871 Green 173.4 10.926 Blue 108.128 11.748 Pasture Sample 4 Mean Pixel Value Standard Deviation Red 198.363 9.629 Green 207.759 10.881 Blue 134.204 11.83 Pasture Sample 5 Mean Pixel Value Standard Deviation Red 197.046 14.712 Green 207.816 13.067 Blue 135.054 14.474 Pasture Sample 6 Mean Pixel Value Standard Deviation Red 110.019 6.96 Green 163.79 8.215 Blue 100.187 11.92 Pasture Sample 7 Mean Pixel Value Standard Deviation Red 126.238 9.459 Green 168.753 9.56 Blue 100.17 12.073 Pasture Sample 8 Mean Pixel Value Standard Deviation Red 117.301 10.329 Green 169.254 11.042 Blue 104.371 12.349 Pasture Sample 9 Mean Pixel Value Standard Deviation Red 126.357 8.542 Green 183.48 9.611 Blue 110.941 12.799 Table 23: Pasture sample values 87
From the above samples, samples 4 and 5 were of freshly cut fields, reflected in the high mean pixel value for the red colour band (almost 50% higher than the average for the image as a whole). This gives areas of freshly cut pasture a unique trait in a high red colour band mean and low standard deviation for a given polygon. This trait also distinguishes cut pasture as the red colour band mean values are over 35% higher than those of pasture in general. The mean green colour band pixel value for pasture is 25% more than that of the image as a whole; and when matched to a low standard deviation gives another strong indication of the type of land use. The above data is useful for indicating the presence of earthworks and landscaped areas within a small polygon. With a key calibrated to identify polygons containing the above values it should be able to accurately calculate the percentage of land area given to pasture; and also improve the accuracy of any process ran on imagery (in conjunction with vector data) by reducing the number of polygons for analysis. Pasture also serves an important part in the algorithm this thesis is attempting to map out. This is for two reasons. Its uniform property (in terms of standard deviation of the pixels form mean values and also the relative proportional difference between those mean values and the mean values for other land coverage types) allows it to form the bases of keys generated at the beginning of image processing to identify land use with. The second reason is that in an Irish context it is by far the biggest form of land use and any algorithm processing aerial photography (even in semi urban areas) will have successfully identified the majority of the surface area by correctly flagging area polygons of pasture. Of the remaining areas most can be identified by polyline coding from the vector data leaving the study with a higher chance of success in obtaining useful information about the remaining areas. It is useful to divide the classes of pasture into the two categories identified tin the samples above –that is to create a separate category of cut pasture for the purposes of executing a comparative survey of polygons in the aerial photography. With this in mind the following test samples were compared against the spectral values of both. 88
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
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 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: 3.9 Pasture Figure 15: Typical Past
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 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
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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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