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An iterative run through a practical application might be: • Obtain the mean for unique known features (e.g. the blue channel in buildings). • Subtract these known features from the area polygon. • Obtain the mean values for the remaining pixels. • Compare these values relative to the known features and determine the most probable land coverage. Following this initial sampling of roof values, it emerged that the values were not consistent enough for reference to determine adjacent land use types (though the fact that the polygons are coded vector polygons means they can be excluded form pixel value searches of small area polygons. One aspect of the pixel values which did emerge, and has potential for further analysis, was the clear division and pixel count peaks for roofs with a pitch. Taken further, these values, in terms of their relative pitch and matched to the set of proportional values that this study sets out to establish, the ability to determine roof pitch could be included. This would necessitate a separate study (over a large quantity of buildings across a wide geographical area and will not form part of this study; although the algorithm outlined in the thesis would be an extremely beneficial for such a study. The sampling for roof values in proportional reference to adjacent areas took three samples of separate land cover types and the road polygons adjacent to them. The three areas analyzed were; a section of pasture close to a farm dwelling, a section of mixed forest close to another dwelling (with several pitches in the roof) and a section of cut pasture close to a flat roofed shed. As mentioned above the sampling did not produce any useful data for the algorithm (except to eliminate the possibility of buildings being used for spectral reference). It did, however, outline the potential for a separate building analysis based on roof pitches. The following few paragraphs outline the results of the comparison. 83
Roof test sample 1 Mean pixel value Standard deviation Red 171.121 25.028 Green 202.818 22.936 Blue 178.773 17.709 Roof test adjacent sample 1 (pasture) Mean pixel value Standard deviation Red 113.004 7.169 Green 156.632 8.779 Blue 99.867 11.634 Table 20: Roof test sample value 1 The first sample took an area of pasture close to a dwelling where the pasture gave expected values for the type of land cover (which is outlined in the pasture sampling in this section of the thesis). The roof sample showed a relatively high level of standard deviation across the colour bands (although low by roof value standards) and the mean colour values returned, although high for a small area, could not be used to uniquely identify other features. The double peak (of shade and light) in the red and green colour bands indicated that that the roof had a high pitch. One problem which makes using this type of surface difficult is the degree of reflection that can take place form the relatively smooth surface of roof covering – further distorting spectral values. Roof test sample 2 Mean pixel value Standard deviation Red 121.394 42.959 Green 144.111 41.557 Blue 128.182 32.159 Roof test adjacent sample 2 (mixed forestry) Mean pixel value Standard deviation Red 70.209 34.505 Green 104.58 33.996 Blue 86.887 20.602 Table 21: Roof test sample value 2 84
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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
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 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: These mean greyscale pixel values f
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 and 138: Figure 57: Aerial view of marsh tes
Page 139 and 140: 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
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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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