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making up the boundary, e.g. fence/ forestry/ building/ water). By identifying a value that this shade should fall under it is possible to derive a corresponding relationship in the histogram and adjust the results of the study accordingly. The final result of the authors work in Costa Rica was only “moderately successful” (Cordero-Sancho & Ader, P.1589). This would seem to have been largely due to the altitude with which the imagery they used was captured, by their own admission the results would probably be better had the imagery been low flown or of better resolution. The methods the authors employed in the study are useful examples of how inconsistencies in results obtained during the process of completing this thesis might be countered –in particular potential methods for reducing the effect shade might have on altering the results could be applied. In this thesis, as with most automatic aerial imagery analysis, the classification of target areas in the photo (usually according to spectral values) is a vital part of the process. One solution is to develop a key to differentiate between features. The level of detail that can be obtained can be quite precise, but is dependant on the resolution of the imagery and the complexity in the patterns of distribution of the target. This is illustrated by Megan Lewis 1998 study of vegetation communities in an area of westerns New South Wales. In this study she attempted to develop a key to differentiate between vegetation types. The problem she was attempting to counter was that of identifying particular species. She noted that existing aerial analysis could detect the presence of vegetation and in an attempt to improve this process she divided these species into colour bands. She took sample plots of 250sqm corresponding to 8*8 blocks of pixels in a relatively homogenous area and calibrated the relationship between field verified data and fifty of these blocks (using them as training areas for the study). The study used 12 colour bands which were allocated into nine classes and identified a link between these and vegetation classes. In the conclusion to the study the author noted that it was possible to portray sub-polygon variation using pixel-based imagery. In order to complete this study it was necessary to make the best use of the available imagery. This imagery can benefit from preprocessing in order to highlight the areas being captured. One method for achieving this might be to 155
apply an algorithm to colour the data so as the target areas are easily captured. This is something which was identified by Thomas Knudsen in his 2005 study of pseudo natural colour aerial imagery for urban and suburban mapping (and in previous studies by the same author). In his study he suggests an algorithm for automatic urban and suburban aerial image interpretation. The paper uses test data from (pseudo) natural color images used in traditional photogrammetry (as opposed to airborne four channel imagers). His aim is to discriminate between vegetation and human made materials, which was also one of the aims of this thesis. The author cites the relative importance of separating vegetation (which he considers to be void of mapping objects) and human-made materials in respect to automated photogrammetric mapping. It is worth noting at this point that imagery captured in the near-infrared band is generally indicative of vegetation and Knudsen’s work is an attempt to identify this band using only aerial photography captured using red, green, blue three channel instruments. His work is very relevant to this thesis as over the course of his study he identifies a method of obtaining “excellent” (Knudsen, P.2691) reproduction of grey surfaces (which in an urban area correspond to paving and exposed rock). The author takes a look at three algorithms in terms of their effectiveness in discriminating between areas in scanned aerial photograph. The first is a pseudo natural color algorithm developed by the author in a previous study (Knudsen 2002) where he managed to create a blue channel based on green, red and near infrared values and left the green and red values as captured. This allowed for good reproduction of red surfaces (which corresponded to roof surfaces in the Danish sample data) but suffers slightly from haze effect. The second algorithm the author considers is one which creates a blue channel between green and near infrared and a green channel form similar values and leaves the red as captured. This, similar to the first algorithm, gave good reproduction of red surfaces and of vegetation, but failed in reproducing clear grey surfaces. The third algorithm the author considers involved swapping the green data for blue, the near infrared for green and leaving the red as was. This allowed him to reproduce grey surfaces accurately (making them stand out in the 156
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
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Figure 3: Road area and vector data
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(The previous samples were compared
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Road test sample 2 Mean pixel value
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3.2 Water Figure 4: Typical Water A
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The purpose of this thesis is to id
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present in the target area (or its
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possible to quickly process each se
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Marsh Sample 1 Mean Pixel Value Sta
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Marsh test Sample 1 (Pasture) Mean
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form both pasture and road/ paving
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Forest Sample 1 Mean pixel value St
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Coniferous forestry test sample 1 (
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whole was not analyzed but individu
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infrared signatures to identify the
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sampled in this study. In relation
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3.6 Track Figure 9: Typical Track A
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surface area that might have been i
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(Phynn et al, 2002). In this study
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3.7 Shade Figure 10: Typical Shade
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currently available for all areas a
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trends of bordering polygons mentio
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3.8 Roof Areas Figure 12: Typical R
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Figure 13: Distribution of Building
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These mean greyscale pixel values f
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Roof test sample 1 Mean pixel value
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3.9 Pasture Figure 15: Typical Past
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From the above samples, samples 4 a
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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
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cycle being suggested here to prese
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4 Testing This chapter describes a
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Figure 17: Creating the ASCII file
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Figure 20: Green colour band for pa
Page 111 and 112: Figure 23: Green colour band for pa
Page 113 and 114: The peak for the green colour band,
Page 115 and 116: Figure 30: Green colour band for pa
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
Page 149 and 150: This trend was continued for the bl
Page 151 and 152: The green colour band pixel count a
Page 153 and 154: 4.5 Conclusion Image segmentation i
Page 155 and 156: 5 Literature Review The goal of thi
Page 157 and 158: shown give a more detailed picture
Page 159 and 160: dependencies” (Kettling, P.330).
Page 161: identifying coffee plantations from
Page 165 and 166: In 2002 S. Phinn, M. Stanford, P. S
Page 167 and 168: with the authors work for a softwar
Page 169 and 170: ased analysis, solely spectral base
Page 171 and 172: 16*16 pixels as urban or nonurban.
Page 173 and 174: 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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