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3.1 Roads Figure 2: Road area and surrounding detail This part of the study looked at sample sections of road (tarred/ hard cover) to see the relationship between the mean spectral value in these areas and the image as a whole. In general terms areas of road appear lighter than other parts of an aerial image due to increased reflection along the surface and this was borne out by a mean greyscale pixel value of 30% above the image average across the three colour bands. The study involved using <strong>Open</strong>EV (open source raster imaging tool based on the GDAL library) and PCI Geomatics Geomatica geospatial viewing application. The files were exported as GeoTiff files from the original image using the GDAL_export facility. In all ten regions were sampled. These sample areas were taken from within the road polygons (as opposed to sampling the entire polygon) in order to identify true baseline data for these features. Sampling the entire feature would also have meant including areas obscured by tree cover, and necessarily biased the results –the intention of this part of the study was to create a benchmark against which tolerances for deviation could be included. The ten sample areas were taken from a series of roads in the south east of the image and three sections of the national primary road running along the north of the image (the pixel representation in the example below is to illustrate the area being sampled but is at a lower resolution than was used in the study). 35
Figure 3: Road area and vector data In general terms the sample areas had an equal distribution of values across the red green and blue colour bands when compared to the image as a whole and it was not possible to discern any unique variation on the proportion of pixels in each of these bands contained in the road polygons. The results, however, did not deviate to any great extent between the samples and the mean greyscale values in the samples remained consistently higher than those of the image as a whole. This was significant as the variation remained at around 30% higher for each band in each sample (34.4 on av. in red, 26.6 on av. in green and 36 on av. in blue). Road value sample 1 Mean pixel value Red 215.5 Green 243 Blue 194.7 Road value sample 2 Mean pixel value Red 206.444 Green 234.5 Blue 157 36
Page 1 and 2: Automated Aerial Image Analysis usi
Page 3 and 4: 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
Page 11 and 12: • Something capable of handling i
Page 13 and 14: of a control to calibrate most of t
Page 15 and 16: 1.2 General Introduction and Backgr
Page 17 and 18: the higher the chances of successfu
Page 19 and 20: overlaid aerial imagery. This is du
Page 21 and 22: 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: 3 Sampling for the Baseline Image K
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 and 96:
From the above samples, samples 4 a
Page 97 and 98:
For the track/ hard cover the diffe
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3.10 Rough Pasture Figure 16: Typic
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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 111 and 112:
Page 113 and 114:
The peak for the green colour band,
Page 115 and 116:
Page 117 and 118:
Figure 31: Vector data for rough pa
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Figure 34: Green colour band for ro
Page 121 and 122:
Page 123 and 124:
The peak for the green colour band,
Page 125 and 126:
Page 127 and 128:
The first sample area came from a p
Page 129 and 130:
Figure 47: Red colour band for mars
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Figure 50: Aerial view of marsh tes
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from which any variance would flag
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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
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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
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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