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2.1 Initial Inputs The following four sections describe the work in terms of steps through the algorithm being proposed. This first section introduces the initial inputs required to define the region of interest to be analyzed: This study attempts to find an automatic method for image analysis using vector data as a reference, and in particular small area polygons and their associated coding. At the beginning of the proposed algorithm a user is required to input a region of interest for the process. This corresponds to the geographical area in which the user is interested. The most convenient way for someone to do this is to manually select the area from vector data or photography (or a combination of both displayed together) displayed in a window on a pc. The result of this should be a set of co-ordinates from which the study area can be extracted. The user also needs to input a sample target area for the study. This can be one of a set of values developed in this study or may take the form of a particular variation (such as a distinct type of crop etc.). In the second case a sample of the required value is needed. This can be obtained in the same way as the first part of the region of interest selection where, as mentioned above, the user manually selects the target area from a viewing window and the output is a set of co- ordinates. A second way this target data might be obtained would be from a co- ordinate set input from a field survey completed using mobile GPS device. In this case the co-ordinates first need to be converted to the Irish Transverse Mercator framework, so as to allow the process to match them to the projection used in the photography and vector mapping. The general flow of the first part of the algorithm being suggested is user inputting the region of interest and a required value from the image analysis, which are then converted to a format which can be compared against the data. In the case of the software used in this thesis this takes the form of a simple ASCII file containing a co-ordinate set, but a common format might also be the .shp file used by ESRI. 23
The software required for this step in the proposed algorithm includes an application for viewing and analyzing raster data and capable of performing transformations on sets of co-ordinates. For this study four sets of libraries were used, packaged into open source applications known as <strong>Open</strong> EV, Mirone and GDAL. The vector data was clipped using an application which forms part of a geographical information system called Radius Vision. All the processes necessary for the first part of this algorithm can be performed using GDAL, with the exception of clipping to an irregular polygon, which is still under development (GDAL, 2010). There is a license requirement for the Radius software, which was used in this study for the step involving the user selecting the extent of the region of interest in the vector mapping. It should be noted that this can also be completed using any other vector mapping tools such as Arc<strong>View</strong> (which would create a .shp file). Another alternative is for the user to manually create an ASCII file of co-ordinates (with the convention of easting northing, separated by newline). This alternative can be frustrating for the user and the suggested process is to make use of software capable of designating the region of interest through a viewer. The data required for the aerial image analysis is ordnance survey ortho-rectified colour aerial photography and matching digital mapping. The archive of aerial imagery goes back to the 1970s and the algorithm being suggested is designed to operate with any run of photography so users can discern dispersal patterns over time through successive photography dates. The process, however, makes use of the three colour bands present in colour photography and is limited to photography with the red, green and blue colour bands. The vector data used will take the form of 1:5000 or 1:2500 scale digital data and it is this data which forms the basis of the search process. The vector data has the region of interest divided into a mosaic of small area polygons the majority of which are coded according to their use or content. The aim of this thesis is primarily to automatically register additional data for those of unknown use type –and secondly to flag those of known use type with specified (spectral) anomalies from user requests. In order to be successful the data requires the coding, which may be useful to consider as a data hierarchy at this stage in the process. The following hierarchy is only for illustration. In practice each polygon will be analyzed according to its spectral 24
Page 1 and 2: Automated Aerial Image Analysis usi
Page 3 and 4: Contents ii Page Certificate of Aut
Page 5 and 6: List of Figures iv Page Figure 1: A
Page 7 and 8: Abstract This study sets out an alg
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: Aerial imagery is a form of databas
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
Page 59 and 60: 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 (
Page 67 and 68: 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
Page 75 and 76: surface area that might have been i
Page 77 and 78: (Phynn et al, 2002). In this study
Page 79 and 80: 3.7 Shade Figure 10: Typical Shade
Page 81 and 82:
currently available for all areas a
Page 83 and 84:
trends of bordering polygons mentio
Page 85 and 86:
3.8 Roof Areas Figure 12: Typical R
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Figure 13: Distribution of Building
Page 89 and 90:
These mean greyscale pixel values f
Page 91 and 92:
Roof test sample 1 Mean pixel value
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3.9 Pasture Figure 15: Typical Past
Page 95 and 96:
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
Page 107 and 108:
Figure 17: Creating the ASCII file
Page 109 and 110:
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
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Figure 55: Red colour band for mars
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Figure 57: Aerial view of marsh tes
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4.4 Bog Test The sampling for areas
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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).
Page 161 and 162:
identifying coffee plantations from
Page 163 and 164:
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
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H. van der Werff and F. van der Mee
Page 177:
Shen, S. S., Badhwar, G. D., and Ca
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