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The Guangzhou study is useful in proving that a relatively high level of accuracy can be obtained when automatically capturing urban data over a large scale. This data was improved on by making use of a smaller area for this thesis using higher resolution imagery and additional indicators (vector and code data imported from large scale mapping). This thesis is fortunate not to have the variety in landscape patterns that previous studies have had to contend with, which meant that a high accuracy level was possible. In much of the available literature the studies are completed on a very large scale (as in the previous two papers) with very specific data in mind. They attempt to identify particular plant species or types of urban development. The methodology being used for this study may benefit from applying some of those techniques to a more stable sample. There are several advantages present in the area being targeted. The temperate nature of the Irish climate means that areas which are not developed will be covered by vegetation, so may fall into the near infrared category, while areas under development should display values consistent with earthworks or paving. At the outset of the study it was expected that most roofing would fall within a relatively small range of colors and could be used to calibrate the search. This was not the case, however, tarmac road data proved a useful replacement in terms of consistent spectral property throughout the image. The thesis looked at a very specific aspect of this body of knowledge and attempt to bridge the gap between automatic aerial data capture and traditional photogrammetric methods. It is noted that in most of the study areas the authors did not benefit from the availability of large scale coded vector data and the premise for the study was that if this is available then the accuracy of automatic capture can be increased. At the core of all of the literature mentioned in this review is the classification of imagery (with the exception of the point signature algorithm proposed by Duradev). In the course of this review I encountered one study which posed one of the same questions that are considered in this thesis; can the use of geometric information increase classification accuracies in aerial image processing? This study (Bellens et al, 2008) proposed a method of morphological profiling to improve the data capture. The authors identified “substantial improvement” (Bellens et al, P.2803). The study points out that urban areas such 165
as roads and car parks are so similar spectrally that they cannot be separated by a spectral analysis alone. They further divide spectral analysis into pixel-based or object-based. The object based methods group pixels together in a meaningful way, something which the authors identify as a difficult task. It is the intention of this thesis to use the former method. The authors identify a method for automatically obtaining structuring elements to help construct the segmentation (such as solid rectangular objects, roofs etc.), allowing a shape index to help extract man-made structures from the image. One observation that can be made from the available literature on automatic aerial image processing is that even with accurately segmented imagery (such as clearly divided vegetation and urban areas) a considerable amount of work is involved in training the algorithms to classify target areas. The creation of a standard key, which can be extended by the user, became one of the main focuses of this study. This thesis enables the user to reduce the workload by presenting a method for quickly calibrating an automated search. 166
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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
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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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Figure 31: Vector data for rough pa
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Figure 34: Green colour band for ro
Page 121 and 122: Figure 37: Green colour band for ro
Page 123 and 124: The peak for the green colour band,
Page 125 and 126: Figure 44: Green colour band for ro
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 and 168: with the authors work for a softwar
Page 169 and 170: ased analysis, solely spectral base
Page 171: 16*16 pixels as urban or nonurban.
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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