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user with a pre calibrated image key of expected spectral values it is possible to automatically classify aerial image sections. It should be noted that this study was confined to a specific type of vector data (ordnance survey) and the landscape of small polygons with a single land use may not apply to all landscapes. However, the study proves that large scale vector data can be used to simplify aerial image processing. 147
5 Literature Review The goal of this thesis is one that is in line with most work completed using remote sensing processing in that it is looking for traits in aerial imagery which can be used to derive useful information about the surface of the earth. One of the early studies of aerial image interpretation (Kittlers ‘Image processing for remote sensing’ paper) described the process as “the interpretation of image segments that exhibit similar statistical properties” (J.Kittler, 1983). One addition that could be made to that definition is that, for the majority of studies in this field the interpretation should be automatic, or as close to automatic as to make interpretation of a large volume of data viable. There is a vast body of work available which documents various methods for interpreting aerial and satellite imagery. In general terms there is always a focus for each study, e.g. identifying coffee plantations in Costa Rica (Corado-Sanches and Sader, 2005) and this influences the methodology. One result of this is that there are a large variety of methods employed. This literature review considers a representative sample of these in terms of their focus, in other words treating work that uses patterns or shapes as one category, spectral deviation for agricultural/ forestry purposes as another and urban analysis as a further category. In terms of previous studies, the ones that are closest to what this thesis is attempting is the body of work that has been completed on what has been termed ISAs (impermeable surface areas, or, more usefully, hard ground). The focus of these works is to identify the percentage of hard ground within urban areas which can then be used in modeling flood events. During the early part of the study I made use use of the SWAP technique recommended by T.Knudsen in his 2005 analysis of color in aerial imagery to identify grey areas in the test imagery (Figure 5: Water Area Image Modification). In the context of the data being analyzed by this thesis (Irish peri-urban land parcels) these grey areas within the image can be made to correspond to hard ground. Before considering the body of work underlying this thesis it is probably useful to answer two questions which the reader might ask; is this not just 148
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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
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
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: 4.5 Conclusion Image segmentation i
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 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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