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lue. Another indicator present in all the samples was the fact that there was an increase in disparity between the red and green colour bands -18.2% for the image as a whole, compared to almost 30% across the samples. It is the red band which is the most valuable indicator of this type of ground cover, with over 25% lower value from the image mean. While coniferous will have been captured and indicated as a level in the OSI vector data, smaller areas of this type of ground cover will be typically present along the margins of small area polygons close to urban areas. In particular a polygon closed by what is called a peck in the vector data layers could be analyzed for the mean red colour pixel value converted to greyscale and compared to the image whole. If the variation is close to 25% lower, then it is probable that either this type of tree cover is present. It should be noted that further spectral analysis (involving swapping of the colour bands) can present additional indicators, which will be applied later in the study. There are several implications of being able to identify coniferous vegetation in an urban area; it indicates permeable surface area for planning/ flood modelling. In the context of this thesis it allows a section within the study area to be identified and adjoining areas to be measured against; for example, once the presence of coniferous vegetation is detected image processing could be applied to eliminate this from the result set from the target polygon –allowing another analysis to be run on the remaining surface area. 57
Coniferous forestry test sample 1 (tree/ shade/ pasture mix) 58 Mean pixel value Standard deviation Red 81.824 35.235 Green 118.246 34.475 Blue 92.287 17.189 Coniferous forestry test sample 2 (pasture) Mean pixel value Standard deviation Red 122.813 9.725 Green 174.274 12.991 Blue 105.527 12.604 Coniferous forestry test sample 1 (bog) Mean pixel value Standard deviation Red 115.672 9.270 Green 133.684 9.933 Blue 105.059 11.881 Table 10: Coniferous forestry test sample values Three sample areas were chosen to match the data from the coniferous sample areas against. The first of these does not conform to the vector polygons against which the proposed algorithm operated, but was chosen for its mix of ground cover so as to provide a worst possible combination against coniferous values. In other words the distinguishing features for coniferous (outside the vector coding, this sampling was only to test the values relative to samples around the image) of high levels of standard deviation in the red and green colour bands would not be a useful comparative feature as the sample contained a variety of ground cover. The mean and standard deviation alone did not provide strong indicators of the ground cover type but the sample did demonstrate the usefulness of histogram data. The pixel count for the red and green colour bands displayed two clear spikes when presented as a histogram, corresponding to the expected values for both pasture and shade. This presents the possibility of determining a relative proportional (to the polygon size) pixel count flag which would indicate the percentage of land type within an area of mixed use. As was mentioned at the introduction the basis of this study is the referencing of areas within the aerial imagery by small vector
Page 1 and 2:
Automated Aerial Image Analysis usi
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Contents ii Page Certificate of Aut
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List of Figures iv Page Figure 1: A
Page 7 and 8:
Abstract This study sets out an alg
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The process was designed so that it
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• 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 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: Forest Sample 1 Mean pixel value St
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
Page 81 and 82: currently available for all areas a
Page 83 and 84: trends of bordering polygons mentio
Page 85 and 86: 3.8 Roof Areas Figure 12: Typical R
Page 87 and 88: Figure 13: Distribution of Building
Page 89 and 90: These mean greyscale pixel values f
Page 91 and 92: Roof test sample 1 Mean pixel value
Page 93 and 94: 3.9 Pasture Figure 15: Typical Past
Page 95 and 96: From the above samples, samples 4 a
Page 97 and 98: For the track/ hard cover the diffe
Page 99 and 100: 3.10 Rough Pasture Figure 16: Typic
Page 101 and 102: The identification of rough pasture
Page 103 and 104: 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: Figure 23: Green colour band for pa
Page 113 and 114: The peak for the green colour band,
Page 115 and 116:
Figure 30: Green colour band for pa
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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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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