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algorithm is accompanied by sample C code which can be used to test it. I believe it could be used if a method of spectral analysis of aerial photography could be developed to return sharp enough edge detail to identify the component points of these polygons. This seems unlikely in relation to the imagery (and processing techniques) that are currently available and the algorithm is probably of more use in a situation where the vector detail had already been manually captured (in which case the appropriate building code should also be present). Much of the other work involved in manipulation of polygons could be said to fall under the banner of graphic editing of GIS data (as could also be the case with Duradevs algorithm). This type of work (physically manipulating and extracting specific polygons in vector format) would be of particular significance it pattern analysis was being used in this study. If particular patterns could be identified then algorithms for clipping and determining intersections between polygons, such as the one developed by Kui Liua et al in 2007 would be a central part of the process. This would then mean the study would take polygon edges as the basis for captured data and perform calculations to construct an output polygon. As these polygons have already been manually captured, this body of work slightly less relevant. That is not to say that they would not be of central value to an automated image processing technique should it become viable. In terms of studies this concept has been the focus of a lot of effort -such as Pal & Foodys 2009 feature selection study that showed that accuracy of classification declines with additional features when using support vector machines. The fact that an underlying verifiable automatic technique for identifying change in photography and converting it to accurate vector data has not been yielded from these studies indicates that it is probably something that will always be specific to the terrain being analyzed. This work is beyond the scope of this thesis so it focused on an aspect of polygon identification that could be applied to a more general spectral analysis. One previous attempt at this is H.van der Werff and F. van der Meer’s 2008 study into shape based algorithm for identifying spectrally identical objects. In this study the authors took a look at the potential of shape signatures in aerial imagery in order to establish a means of identifying and classifying the object. They look at three broad methods for this; solely shape 161
ased analysis, solely spectral based analysis and a combined “spatial-spectral classification” (H. van der Weff & F. van der Meer, P.251). These studies are slightly different from the one being undertaken in this thesis in that the shape and classification of much of the data will already be known however, the study is useful to this thesis in that it suggests the potential for a method of identifying new farm buildings based on a similar classification. The authors are seeking a method to enhance pixel-based spectral classifications (as will be used in this thesis) by adding spatial information. It is worth noting that the results of the study were not satisfactory in terms of automatically correctly identifying features. The first step the authors used to determine the shape of the areas being examined was to “seed” (H. van der Meer & F. van der Meer, P.252) the object. This involved beginning an object with a single pixel of a set value and increasing the size of the area until a spectral variance in a non-overlapping 3*3 pixel occurs. This part of the study continued until all the image pixels were segmented into objects. The authors noted that size was a factor at this point and objects of 500 pixels or less were more successfully determined. The study itself was looking at parts of Alaska, and the objects being classifies were water bodies; i.e. separating streams from ox-bow lakes, thaw waters from rivers and sediment rich water. This is a difficult task due to the relative random nature of these shapes when compared to a well defined linear pattern that can be observed in the Irish landscape. The authors conclude (in the case of water bodies) “(that) an object should consist of approximately 500 pixels at minimum to be able to use the absolute value of shape measurements” (H. van der Meer & F. van der Meer, P.257). The authors created a combined analysis method by classifying shapes according to threes spectral bands from the imagery being used and comparing the results against the pixel based shape measurements. Using these results they were unable to distinguish between the water bodies being considered by the study and the authors suggest that further research is required to better combine the two (shape and spectral) classifications. In some ways this thesis is a continuation of this, in that it will be using a spectral analysis in combination with spectral signatures (in the form of previously captured and coded vector data). The aim the authors had was to established a means of measurement using an “unbiased software 162
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
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The peak for the green colour band,
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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 and 162: identifying coffee plantations from
Page 163 and 164: apply an algorithm to colour the da
Page 165 and 166: In 2002 S. Phinn, M. Stanford, P. S
Page 167: with the authors work for a softwar
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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