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distances from the centre to divide the imagery. This process allowed the authors to determine the nature of ground cover with a high degree of accuracy, revealed by random point sampling. A description of similar studies and methods is contained in the literature review at the end of this paper. The type of preparation for the analysis required to determine the appropriate extent of image segments required for similar studies can be difficult to automatically include in a study. For example, the knowledge of the availability and accuracy of transport data and how to use it to segment the photography such as in the previously mentioned study (Zhou & Wang, 2007). An alternative to this approach is to use vector data of a known accuracy for the image segmentation. This approach is something which depends on the availability of vector data and confines the focus of this study to an Irish context. One of the benefits of applying an algorithm, which involves automatically segmenting an aerial image into small area parcels, is that it creates a platform on which further image analysis can take place. This study takes a set of ASCII coordinates from the vector data and clips the imagery. These parcels are then classified according to their land use type. A user could then use these classifications to target specific sets for interpretation. For example, determining the type of growth present in marsh areas by using the set of polygons for marsh returned to identifying the percentage of the pixel count corresponding to the expected value for the growth There are a couple of pre-requisite data sets for running this type of analysis and a section from both of these sets (just west of Oughterard, Co.Galway) was used for this study. The first requirement is digital vector data from the Ordnance Survey and the second is colour (RGB) photography stores in GeoTiff format and projected using Irish Transverse Mercator (to match the vector data). It is possible to automatically re-project the imagery using GDAL_transform (GDAL, nd.) given other projections, but this was not used in this study. The software requirements are: • Something which can manipulate and interrogate vector data files. 3
• Something capable of handling irregular polygons within a coordinate system. • Software capable of analyzing pixel values. In this study a commercial package called Radius vision was used to export the coordinate set for each small area polygon. The processing of the image polygons was completed using the Mirone MATLAB based framework tool developed in the University of the Algarve by Joaquim Luis (Mirone, 2009). The histogram values for the segmented image sections was obtained using PCI Geomaticas geomatica package. For each polygon tested in the study the following steps were taken, which are the basis for the proposed algorithm: • Extract the point data which surrounds the polygon(s) within the region of interest. • Import the point data into a software procedure to clip the aerial imagery and save the segmented image in GeoTiff format. • Run a histogram analysis for the image segment. • Run comparison procedures to classify the segment. The point data mentioned above refers to controlled data points which indicate fixed x and y positions on the ground and can be used to analyze the imagery. Attached to those points are vectors and coding which, for much of the image, indicate the type of land use present, e.g. forestry, water, buildings, etc. The comparison procedures mentioned above refer to values obtained during a sampling process conducted in the early part of this study. This sampling first took sections from known polygons and recorded the spectral values for these samples in order to calibrate an image key. Samples for parcels of types not coded into the vector data were then taken and the percentage variance between the two sets was recorded. The sampling for the study was completed on ten separate types of land use. Five of these types were identified from the vector data, while the remaining types were identified using the techniques developed in this study. Separate sections of 4
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: The process was designed so that it
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 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 (
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whole was not analyzed but individu
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infrared signatures to identify the
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sampled in this study. In relation
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3.6 Track Figure 9: Typical Track A
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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
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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
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Page 113 and 114:
The peak for the green colour band,
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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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The peak for the green colour band,
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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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