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platform and used as a means of accessing the raster data from the geoTiff imagery. It is necessary to access GDAL in order to open the geoTiff using the appropriate ITM coordinates for the search area point set being searched. This choice of access to the raster data should not suggest that GDAL and OpenEV are unique. Similar software exists that could also have been used in the study. Another example is the ImageTool open source software library. This was developed in the 90’s in the department of dental diagnostic science in the University of Texas, and written using C++. GDAL and OpenEV were chosen in preference because the data returned could be more easily modified to striate histogram and statistical data with these libraries due to the larger body of work contained within them. One of the most important software considerations was flexibility (and extensibility), as ideally the users of any suggested methodology would modify the process to suit their particular study. As was mentioned above, the preferred option was to build a top down processing tool tailored to the methodology which would accept methods from other libraries to create plug in/ additional functions. For this study OpenEV is substituted for that purpose. Outside of the basic software, two other core components exist. These are the data relating to the specific polygons being extracted and the key to represent the colour values being studied. As with most aerial image analysis studies, defining and validating this key forms the major part of the work involved. The process is aided by the availability of data which could be classed as controlled, that is the knowledge of areas of roof and water, which can be used to reference the other values in terms of their deviation from these known values. As mentioned at the start of this general introduction, a commercial geographic software package was used to extract the coordinates from the vector data (a vital first step in the algorithm). This particular software was not chosen for any specific capabilities other than the fact that I already had a licence and wanted to focus on proving the premise of the study (as opposed to executing the function over a specially tailored package). There are a number of commercially available image processing packages which have relevance to this study. One of these, SOCKET SET, could potentially assist the study. This software is a photogrammetry package developed by BAE systems for working on aerial photography. It allows the user to capture three dimensional data points from 11
overlaid aerial imagery. This is due to a system of triangulation based on the position of the cameras when the photograph was taken. As much of the data used to extract the sections of aerial photography being analyzed in this study was captured using this process it is possible that the study could be completed during this point in data capture. I am referring here to map production, and the stage at which line and point data are taken from remote imagery. At this stage in map production it is possible that an-add on process linked to the photogrammetric software would allow the user to run an analysis of the polygon at the moment it is fully captured and coded, which in turn would mean that the area marker and associated polylines could then be given added data of value to the end user (spectral content of the polygon/ percentage land cover/ rough pasture/ impermeable surface area etc.). I decided against investigating this further for two reasons. Firstly it would have involved a difficult and time consuming collaboration with the software provider. Secondly, and more importantly, this country has been fully digitally mapped and photogrammetric work is now confined to update only. This means that the application of any algorithm at the photogrammetric/ data capture stage would be confined to small, mostly urban areas. It is difficult to discuss the manipulation of spatial data without reference to the ArcGIS package of software created by ESRI. This is widely used in both commercial and educational entities for interpreting spatial data. In terms of this study the ArcView package within ArcGIS could have been utilized for image processing once the input files were converted to shapefile format. The limitations imposed by having to obtain a licence for the software (outside of trial/ educational versions) precluded the use of this package. This is not to indicate that the software would not have been a useful tool for manipulating the imagery in the study, only that it was not practical at the time of the study..The value of this study is in allowing new data to be obtained and added to that originally obtained from an analysis of aerial imagery. While the study identifies one means of doing this, in terms of software and operating platform ( i.e. executing the process using parts of the GDAL library and ASCI input files), there are potentially numerous other software processes that could apply. The algorithm, however, is intended to remain as independent of software considerations as possible; being constrained 12
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: the higher the chances of successfu
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
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
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currently available for all areas a
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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
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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
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
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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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