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general terms, of the programming languages used in GIS development (and aerial image analysis), the C programming language is the most widely used to interpret geographic information. Many analysis programmes such as MITAB and Shape Library use C as a means of accessing geographical data. One of the advantages of using C is that processor heavy functions such as the analysis of pixels in order to categorize them into shades and variations from a mean can be best achieved in this language. This is probably most evident by the fact that most of the open source programming projects such as ImageTool or GDAL have all been written using the C programming language. This thesis makes use of a small aspect of these libraries and as such in turn uses the C programming language. This is not to suggest that the default programming language for this type of study should necessarily be C but that in order to make use of the available body of knowledge; previous studies can probably be best extended using C. It is important to note that the main problems encountered when analyzing aerial data/ imagery are those of co-ordinating the imagery so that it can be referenced and analyzed properly. The three main methods of this are geographic, projected and pixel. This thesis uses a mixture of both pixel and geographic. The fact that it is necessary to use both for the relatively simple cutting and analysis of image segments demonstrates the importance to GIS programming of being able to transform coordinates. Geographic coordinates refer to latitiude and longitude, while projected coordinates refer to a flat two dimensional coordinate structure. The C programming language (through the available libraries and its high level nature) provides an accurate means to execute these coordinate transformations. Note: Other options are available, such as the modules in .net –and coordinate transformation is something which can be achieved across all programming languages once the correct math functions are accessed. Another factor which needed to be considered while reviewing programming languages for this thesis was the limitations that would occur due to the programming experience of the author. Ideally, a processing algorithm, written in C with a Visual basic front end, which could also tie into OSI metadata would be the preferred solution. This could then be extended to allow a user to zoom in on a map window, identify a subject area, review the available photography and target 15
a selected study area. This is possible using existing systems but falls outside of what could reasonably be achieved with the available resources for the study. The main purpose of the study is to determine whether the methodology being suggested is applicable and whether useful data can be returned from this type of study. The degree of success indicates the fesability of tailoring an application. The benefits from developing the already well researched methods of analyzing imagery; in terms of aggregating the pixels and deriving statistical data from a selected tile of aerial photography would be limited. This is because there is already a vast body of knowledge dealing with the subject available. In particular I am referring to work such as ImageTool, which, again written in C, was developed in California and is open source. It (along with several other open source image processing projects) effectively interprets imagery in terms of its spectral content. As I suggested earlier, one of the most important aspects of viewing and studying the surface of the earth remotely is the way it is projected. That is to project it into a format which can give valuable information to the user and this is where a large part of this study will focus. The study takes a captured and referenced coordinate grouping (set of data points) and uses these to analyze sections of the earth. These coordinate groupings are definite points along fixed boundaries which form physical barriers in terms of walls, streams, buildings and roads. These could probably be better explained in terms of a bull in a field. In general terms, any polygon which would prevent the bull from escaping forms a parcel, which is then analyzed. This means that the study areas are bounded by a series of fixed vectors/ polylines which are unlikely to deviate over time, allowing the suggested algorithm to be run over successive years of data capture. With a standard mean and control key for spectral values it should be possible to gain an insight into changes in land use in the specific semi-urban areas looked at the study. The study areas could possibly be extended to rural areas over time. The reason why the study does not extend to these areas is that it would have to account for a much larger study area and less well defined boundaries. This would probably only be effectively done using tried and tested values derived from more fixed (ie. no fuzzy data/ hazy boundaries where pixels gradiate and physical features such as man made fences and walls are not present) polygons. 16
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: upper case, beginning with lower ca
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
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
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(Phynn et al, 2002). In this study
Page 79 and 80:
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
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
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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).
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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
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H. van der Werff and F. van der Mee
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Shen, S. S., Badhwar, G. D., and Ca
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