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the image were extracted for the analysis, ranging from three to ten for each area type. The samples were uniform clear examples of each type of terrain, clear of any biasing factors such as shade or overhanging vegetation so as to obtain clear baseline data. The samples were extracted as GeoTiff images and analyzed for their spectral qualities. A full description and tables for each sample are contained in chapter 3, which is divided into sections according to the land use type for easy reference. In general terms the results were what might be expected; large bodies of water (e.g. river, lake) produced a clearly identifiable signature while mixed forestry and rough pasture areas had a higher level of standard deviation than more uniform cover. The areas sampled fell under the categories of; roofs, roads, water, marsh, rough pasture, mixed forestry, pasture, track and shade. Although shade is not a distinct area, values for shade (manually identified from the imagery and clipped for analysis) were used to calibrate the image key so that these could be recognised when found in polygons identified by the vector data. In a similar way values taken as representative for spectral qualities present in roadways, for example, did not include overhanging tree cover which is present in polygons extracted based on the ground revised vector data. The aim of this part of the study was to identify a series of proportional values which could be used to indicate the presence of a land use type for an unknown polygon –for example, a mean red and green pixel values for the known areas of water was identified as 30 and 45% that of pasture; something which an automatic search could use to flag an area being used as pasture. This sampling was not intended to be comprehensive in terms of creating a key for use in every possible automated image search, but was undertaken to prove the potential for automated image processing based on segmenting the images using small area polygons. One surprising result from this sampling was in the values returned for roof polygons. These polygons did contain two identifiable ranges of values associated with the pitch in the roof, where the angle of the light created shade on one side, which might facilitate a process to determine the angle of the pitch. At the beginning of the study I had believed that these roof values would provide enough 5
of a control to calibrate most of the image. However, the variation in the ranges of values from shade to light on the angled surfaces made roof values an unreliable source of control value for the study. Of the known values samples, the most useful in terms of providing a consistent control to base comparative procedures were water, roads and coniferous forestry. Of the unknown (in terms of being automatically identified from vector data) pasture and bog had the most distinct sets of values. The next phase of the study involved testing the algorithm against these identified spectral values to see if the irregular polygons (with internal distorting factors) matched the range expected from the sampling. The testing process followed the outline for the algorithm. Polygons were extracted from the vector data in the form of a set of coordinate points saved in an ASCII file which were then used to create a clipping path to cut the relevant section from the aerial image, which was then saved in GeoTiff format. This file was then analyzed for its spectral content and the resulting range of pixel values was compared to those expected for the land use type. The testing focused on sets of known polygon types for three typical areas (not coded to the vector data); pasture, marsh, bog and rough pasture. It should be noted that this testing section of the study represented an execution of the algorithm but the level of automation can be improved when the vector data is made available in GML format. Coordinate sets for multiple polygons can be extracted in one file with GML format, something which is expected in the next two years. The areas analyzed were polygons containing marsh, bog, pasture and rough pasture. Of these, pasture and bog produced the most distinctive spectral traits and matched expected values, allowing for any comparative procedure to automatically classify them. Both the marsh and rough pasture sets of samples contained high levels of deviation from the mean pixel value across the red and green colour bands with a similar range of values. However, these can be distinguished by a trough between values corresponding to shade and vegetation present in all of the red colour band values for rough pasture. A full description of testing can be found in chapter 4 of this study. 6
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: • Something capable of handling i
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 (
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
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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
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
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Shen, S. S., Badhwar, G. D., and Ca
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