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Figure 77: Blue colour band for bog test 4 In general terms bog is a very useful land use type for inclusion into an automatic aerial image survey such as this one, as it can be quickly identified and logged early in a looping cycle through the spectral values of polygons. As mentioned above, this is dependant on controlled polylines bounding the area, something which is available for most of the country but results may be distorted on remote high ground (identified by 1:5000 mapping and the presence of cropped rock). The purpose of this test was to determine the value of using the vector data to clip aerial imagery into a mosaic of polygons for spectral analysis. As mentioned in the sampling section of the study it is possible to include pattern analysis testing to determine the level of cutting taking place (drains are included in the vector data and could also factored into such a search). 145
4.5 Conclusion Image segmentation is one of the most important parts of automatic analysis of aerial imagery (Zhou & Wang, 2007). A set of reference data is necessary to know where to divide image sections. This can be obtained from a survey input by the user or from spatial data specific to the area being studied (peat, forestry etc.). Ordnance survey vector data provides a comprehensive set of reference points and allows an aerial image to be cropped into small discrete area polygons. These polygons can also benefit from the previously captured coding which identifies many of them as a specific land type. The result of adding this data to an automatic search for specific spectral values is that the user can gain context from known neighbouring polygons and calibrate the specific search accordingly. This in turn means that the process of image analysis can be simplified by applying a generic technique for identifying polygons and refining it to search for a given value. This study looked at the value of cropping aerial imagery into a mosaic of known and unknown polygons. It attempts to automatically derive probable types for the unknown areas based on the known data and a sampled image key. The sampling and testing undertaken during the study indicated that it is possible to derive useful value from a spectral analysis based on a pixel count alone. This was because the vector data introduced into the process reduced the number of possible values that can be attributed to a pixel set –for example, as the extent of forestry is known, similar values returned from an unknown polygon must represent marsh or rough pasture while further analysis of the shape of the range can distinguish between either. The process is possible using open source software but could also be coded into a standalone application, e.g. using the GDAL library and a function to crop irregular polygons. The potential for automation will be supported by the release of the vector data in GML format, from which a large ASCII file of coordinate sets could be fed into the process. By removing the requirement for a user to control areas of the image through the use of the vector data, and by presenting the 146
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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
Page 101 and 102: 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: The green colour band pixel count a
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 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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