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3.4 Coniferous Forestry This part of the study used unprocessed sections of coniferous (commercially planted) forestry to evaluate a deviation from the average for the image that might indicate this type of ground cover. It took seven samples from a total of five areas of this type of this type of forestry in the image. These were then analyzed in terms of mean values through the red green and blue colour bands to determine if there was any unique deviation from other features. As would be expected for this type of ground cover the values for red (and near infra red) were lower due to colour being absorbed by the foliage. This gives a useful indicator for this type of ground cover and provides a comparative value that the target polygons of the study can be compared against. It should be noted that there is also the potential to use a pattern recognition algorithm to accompany any specific search for this type of forestry as uniform rows are a feature of this type of surface cover. The statistics for the survey are in the table below –it should also be noted that the standard deviation remained relatively consistent for each sample area. 55
Forest Sample 1 Mean pixel value Standard deviation Red 96.1975 19.943 Green 125.339 18.6976 Blue 98.4551 18.6976 Forest Sample 2 Mean pixel value Standard deviation Red 87.0853 21.8368 Green 125.905 23.8361 Blue 103.376 17.2807 Forest Sample 3 Mean pixel value Standard deviation Red 97.484 20.715 Green 137.59 21.077 Blue 109.5 14.9791 Forest Sample 4 Mean pixel value Standard deviation Red 73.072 20.762 Green 112.015 22.976 Blue 96.784 16.405 Forest Sample 5 Mean pixel value Standard deviation Red 76.670 23.651 Green 111.181 24.107 Blue 94.424 16.962 Forest Sample 6 Mean pixel value Standard deviation Red 75.534 24.507 Green 113.943 27.153 Blue 97.851 17.693 Forest Sample 7 Mean pixel value Standard deviation Red 72.424 24.194 Green 111.688 27.628 Blue 96.596 18.1686 Table 9: Coniferous forestry sample values An indicator for this type of land cover (as with rough pasture) is the larger scale value for the blue colour band when compared to the red. This was not the case for the image as a whole where the red band produced a mean almost 10% higher than 56
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 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: form both pasture and road/ paving
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
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
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
Page 129 and 130:
Figure 47: Red colour band for mars
Page 131 and 132:
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
Page 137 and 138:
Figure 57: Aerial view of marsh tes
Page 139 and 140:
4.4 Bog Test The sampling for areas
Page 141 and 142:
Figure 62: Red colour band for bog
Page 143 and 144:
Figure 65: Vector data for bog test
Page 145 and 146:
The histogram for the green colour
Page 147 and 148:
Figure 70: Aerial view for bog test
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This trend was continued for the bl
Page 151 and 152:
The green colour band pixel count a
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4.5 Conclusion Image segmentation i
Page 155 and 156:
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
Page 165 and 166:
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
Page 175 and 176:
H. van der Werff and F. van der Mee
Page 177:
Shen, S. S., Badhwar, G. D., and Ca
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