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Figure 47: Red colour band for marsh test 1........................................................122 Figure 48: Green colour band marsh test 1..........................................................122 Figure 49: Blue colour band for marsh test 1.......................................................123 Figure 50: Aerial view of marsh test 2.................................................................124 Figure 51: Red colour band for marsh test 2........................................................125 Figure 52: Green colour band marsh test 2..........................................................125 Figure 53: Blue colour band for marsh test 2.......................................................126 Figure 54: Aerial view of marsh test 3.................................................................127 Figure 55: Red colour band for marsh test 3........................................................128 Figure 56: Blue colour band for marsh test 3.......................................................129 Figure 57: Aerial view of marsh test 4.................................................................130 Figure 58: Red colour band for marsh test 4........................................................130 Figure 59: Blue colour band for marsh test 4.......................................................131 Figure 60: Vector data for bog test 1 ...................................................................132 Figure 61: Aerial view for bog test 1 ...................................................................133 Figure 62: Red colour band for bog test 1 ...........................................................134 Figure 63: Green colour band for bog test 1 ........................................................134 Figure 64: Blue colour band for bog test 1 ..........................................................135 Figure 65: Vector data for bog test 2 ...................................................................136 Figure 66: Aerial view for bog test 2 ...................................................................137 Figure 67: Red colour band for bog test 2 ...........................................................137 Figure 68: Green colour band for bog test 2 ........................................................138 Figure 69: Blue colour band for bog test 2 ..........................................................139 Figure 70: Aerial view for bog test 3 ...................................................................140 Figure 71: Red colour band for bog test 3 ...........................................................141 Figure 72: Green colour band for bog test 3 ........................................................141 Figure 73: Blue colour band for bog test 3 ..........................................................142 Figure 74: Aerial view for bog test 4 ...................................................................143 Figure 75: Red colour band for bog test 4 ...........................................................143 Figure 76: Green colour band for bog test 4 ........................................................144 Figure 77: Blue colour band for bog test 4 ..........................................................145 v
Abstract This study sets out an algorithm for the automatic analysis of controlled (flattened) aerial photography using ordnance survey vector data. It uses the vector to clip the aerial image into a set of small area polygons which are then analyzed for the spectral properties and classified according to the result. The study tests sections of aerial photography from a sample area in County Galway for specific spectral properties. This was to identify the type of ground cover and was achieved using an image key of spectral properties which was developed during the study. This is called training the image key. A testing section shows that it is possible to derive information about the land use type from these areas based on the range of values returned from a pixel count of spectral properties within a small area polygon. The study uses several open source software frameworks to complete the experiment, most notably the MATLAB based Mirone application, but can be extended to any software capable of handling irregular polygons in a projection system. The body of the study is set out in three chapters, the first detailing the process, the second detailing the sampling for unique values and the third details the testing which took place. Chapters 3 and 4 are sub divided into sections describing the research on specific land use types and their spectral signatures. Chopping an aerial image into a mosaic of (relatively) homogenous values, e.g. pasture, forestry, marsh etc., increases the accuracy of automated analysis. This is the first time that a spectral analysis has been attempted using ordnance survey Ireland small area polygons to clip the image. It is of interest to researchers, planners, developers etc., looking to simplify and automate this type of search over a large region. Note: Contact brian.sexton@osi.ie for a set of sample files for training the image key. vi
Page 1 and 2: Automated Aerial Image Analysis usi
Page 3 and 4: Contents ii Page Certificate of Aut
Page 5: List of Figures iv Page Figure 1: A
Page 9 and 10: The process was designed so that it
Page 11 and 12: • Something capable of handling i
Page 13 and 14: of a control to calibrate most of t
Page 15 and 16: 1.2 General Introduction and Backgr
Page 17 and 18: the higher the chances of successfu
Page 19 and 20: overlaid aerial imagery. This is du
Page 21 and 22: upper case, beginning with lower ca
Page 23 and 24: a selected study area. This is poss
Page 25 and 26: 2 Stepping through the Algorithm Th
Page 27 and 28: The process can also be coded into
Page 29 and 30: Aerial imagery is a form of databas
Page 31 and 32: The software required for this step
Page 33 and 34: study area from the photography and
Page 35 and 36: The areas extracted were placed int
Page 37 and 38: ands was detected the standard devi
Page 39 and 40: 2.4 Confirmation The final stage of
Page 41 and 42: 3 Sampling for the Baseline Image K
Page 43 and 44: Figure 3: Road area and vector data
Page 45 and 46: (The previous samples were compared
Page 47 and 48: Road test sample 2 Mean pixel value
Page 49 and 50: 3.2 Water Figure 4: Typical Water A
Page 51 and 52: The purpose of this thesis is to id
Page 53 and 54: present in the target area (or its
Page 55 and 56: possible to quickly process each se
Page 57 and 58:
Marsh Sample 1 Mean Pixel Value Sta
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Marsh test Sample 1 (Pasture) Mean
Page 61 and 62:
form both pasture and road/ paving
Page 63 and 64:
Forest Sample 1 Mean pixel value St
Page 65 and 66:
Coniferous forestry test sample 1 (
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whole was not analyzed but individu
Page 69 and 70:
infrared signatures to identify the
Page 71 and 72:
sampled in this study. In relation
Page 73 and 74:
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
Page 105 and 106:
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:
Page 113 and 114:
The peak for the green colour band,
Page 115 and 116:
Page 117 and 118:
Figure 31: Vector data for rough pa
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Figure 34: Green colour band for ro
Page 121 and 122:
Page 123 and 124:
The peak for the green colour band,
Page 125 and 126:
Page 127 and 128:
The first sample area came from a p
Page 129 and 130:
Figure 47: Red colour band for mars
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Figure 50: Aerial view of marsh tes
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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
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4.4 Bog Test The sampling for areas
Page 141 and 142:
Figure 62: Red colour band for bog
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Figure 65: Vector data for bog test
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The histogram for the green colour
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Figure 70: Aerial view for bog test
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This trend was continued for the bl
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The green colour band pixel count a
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4.5 Conclusion Image segmentation i
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5 Literature Review The goal of thi
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shown give a more detailed picture
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dependencies” (Kettling, P.330).
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identifying coffee plantations from
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apply an algorithm to colour the da
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In 2002 S. Phinn, M. Stanford, P. S
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with the authors work for a softwar
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ased analysis, solely spectral base
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16*16 pixels as urban or nonurban.
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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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