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only by the quality of the remote imagery and the accuracy of the captured data points and associated coding. Looking briefly at some of the other commercially available desktop GIS software, it is intended that the methodologies suggested could be applied to these products. However, due to the limitations involved in both learning to use the software and licensing issues this application of the study was not explored. These products include AutoDesk, Microstation, the ESRI ArcView product mentioned in the previous paragraph, IDRISI and MapInfo among others, such as the 1Spatial Radius platform used to edit the geometric input data being used in this study. All of the above products are useful in the case of updating and editing, that is to say – dealing with change. This study looks at read only data and could be described as a way of interpreting already captured data. One result of this is that the functions required to store and update change polygons and data values are not needed in the proposed algorithm. The ability to connect the statistical data to the unique identifier for the polygon should be enough to allow it to be input as an attribute by the spatial database management system. Outside of analysis the GIS software requirements relate only to coordinate transformation. As a result, while storage (of the statistics) remains a consideration, the necessity for creating, editing or updating (moving points etc.) do not form part of the requirements for the study. Even though a specific analysis tool (in terms of a standalone executable) is not presented in this study it is possible to create one and add to the existing body of open source work. OpenEV, for example, allows for the addition of newly created functions using a Python compiler. This programming language allows a user to interface with GIS applications written in C and has the potential to be a flexible means of accessing C libraries (e.g., accessing GDAL from OpenEV). It has been used to compile different software libraries such as GeoDjango, Thuban, OpenEV, pyTerra, and AVPython. The language itself is not used in this thesis as it added another layer to the process but provides a possible means of packaging an extended experiment. One of the advantages of using Python as a programming language in preparing a GIS application is that the assignment of a variable does not have to indicate whether it is declaring a string, number, list etc. The variables, however, are case sensitive and follow the ESRI using a combination of lower and 13
upper case, beginning with lower case. The acronym for the variable is at the beginning of the name, while the descriptive part follows, beginning with an upper case (Eg. htElev). In addition to modules such as math and string Python also has several geoprocessing modules. One of these, arcgisscripting, accesses all the arc toolbox tools. It should be noted that the geoprocessing object that might be being called is accessed differently, depending on the version of arcGis being used. Another package called gdal (which accesses the spatial data abstraction library being utilized in this thesis) allows for the manipulation of this library. In this Python module the language connects to the original gdal programming language (usually C or an object oriented variation) using a SWIG interface compiler. An example of Python in use in GID can be its application alongside ArcGis; ArcGis was built using hundreds of arc objects such as “featureClass”, “symbol”, “field” etc., each of which has properties and methods accessed by Python using dot notation. An example of this notation is the assignment of a variable name tr = arcGisScripting.create(). Another possible method for implementing the process suggested in this study is through the .NET platform. In particular VB.NET provides a programming language that can be utilized to access GeoMedia software –which is a .NET oriented group of geographic software packages provided by Intergraph. This software allows the user to interact with ESRI shapefiles and also with spatial databases created using Oracle Spatial. It also allows for developed tools to tie in with graphical editing platforms such as Autocad and Microstation which would allow for interpretation of both images and associated geospatial data. This would also mean (given appropriate licence) that the developer could access specific ancillary products for database management (for databases based in Oracle) and others ranging from map production to 3D modeling. This programming language (VB.NET) was not used in this thesis because of the potential licencing issues which may have been involved. This thesis suggests procedures which lend themselves to C as they involve repeated loops of steps, from the pixel analysis to the classification of the polygons identified by the user through the region of interest. These procedures involve processor heavy analysis and lend themselves to being developed in C. In 14
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: overlaid aerial imagery. This is du
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
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
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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,
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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
Page 177:
Shen, S. S., Badhwar, G. D., and Ca
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