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from scratch. For an example of this type of work see Thomas Knudsens 2005 study on aerial image analysis. What is unique about this study is that it attempts to use previously captured data as a basis for further image interpretation. This is something which, from the research into the data and contact with ordnance survey, has not been attempted before for Irish digital spatial data. All of the difficult remote sensing is complete (via the vector mapping) before this analysis begins, and control points, physical boundaries and closed polygons have all been identified. This study presents a method for developing software to extend the work completed, and assist users to identify specific traits in what would otherwise be an impossibly large store of imagery for the human eye to analyze (without using a team of trained analysts). The algorithm proposed here is essentially a way of looking for spectral values in small area polygons and comparing them to known values. The process outlined is for people intending to scan aerial imagery of Ireland for specific spectral properties. It is intended as an additional facility for users of aerial photography. At present it is possible for people to conduct specific research using the photography and a GIS tool. This algorithm is intended to make the process accessible to users who do not have the time or access to the resources or software licences to conduct this type of research. It can be used to identify specific land use types which are outside those currently captured by ordnance digital mapping and serve as an add on tool for anyone using that type of data. The purpose in compiling the data and researching software for the study was to outline a method and as such the application of the method is dependent on the user. In the case of this study a lot of emphasis was placed on the identification of pasture. This is because it is the major form of land use in rural and peri-urban areas and its correct identification helps in limiting the search for other types of cover to a relatively narrow number of polygons. In a similar way someone could take the same steps –identifying unique statistical properties of the pixel count in the types of area being studied and add them to the algorithm. This would involve including the additional search to the cycle of flagged polygons at the end of the third part of the search execution. 19
The process can also be coded into a standalone application or as an extension of existing software for a specific use (such as searching for crop disease). One example might be with the python based raster viewer <strong>Open</strong>EV, where a user is analyzing aerial photography using the package. It is possible to extend the functionality of this to analyze statistical data using the GDAL library. A user concerned with a specific set of spectral values, or wanting to confine the research to a specific area polygon type within the image, could make use of the methods set out here to set the statistical function to return target data only (as opposed to a general application of the histogram function). In broad terms this study is for users of aerial raster imagery and the results of the sampling are based on samples of Irish data. It may be possible to execute similar studies for different regions but the small well defined polygon types with clear consistent (over large periods of time) boundaries are a vital part of the analysis. This is probably a result of relatively small property divisions and rigorous maintenance of the boundaries over hundreds of years and may be unique to Ireland. In short the study is a look at a possible coded routine to analyze the Irish landscape using all the available data. As mentioned in the previous paragraph this study is intended for users of aerial photography. The pre-requisites to this are that it is controlled and has the projection embedded in the file, and the users have access to ordnance survey vector data. Outside of these conditions the algorithm is intended for users who do not have a strong background in information technology as much as those who have a good knowledge of code and could easily convert the proposed steps into routines. The open source software described in the study has a familiar user interface to any GIS package (standard toolbars/ zoom/ measurement etc.) and it is possible for someone to run the algorithm without having to alter any of the steps. Ideally, however, the steps would be converted to add on to an existing piece of software that is being used (Arc<strong>View</strong>, Microstation etc.) so that the user can quickly run through large amounts of data. In this way the routine is designed for anyone who is interested in targeting specific properties of Irish topography that can be defined in terms of their spectral values. These areas range from forestry and agriculture to urban planning. The limitations of the study are in the quality of the imagery and it was shown that some potential applications of spectral analysis 20
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: 2 Stepping through the Algorithm Th
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
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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
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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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