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planet will always have fluid boundaries. In this example the change from marsh to rough pasture is a gradual one, and does not correspond to a single vector. Various solutions such as an additional transitional polygon instead of a linear boundary are simply methods of belting the square peg of a gradual change into a relational database. Although the study is attempting to identify statistical data (percentages of types of land cover) that can be appended to the entry for a given area polygon in a spatial database in this case a bitmap displaying concentrations of values corresponding to marsh might be more appropriate. When the values obtained from marsh were compared to three sample sections from other polygons in the study some unique proportional variations emerged. The purpose of the study is to iteratively reduce the quantity of unknown (in terms of land usage) polygons in the search area by appending values derived from the aerial photograph. The sample areas used in this test were not from any of the original samples used to obtain baseline spectral data for the land type they represent but were chosen to see if a reliable (or at least significant) proportional deviation could be observed. The three sections sampled were pasture (which had been recently cut), mixed forestry (chosen because of the variety of spectral values that this type of cover represents) and paving (taken from a yard surrounding buildings but similar to any of the road and track hard surface areas sampled elsewhere in this study). All three sample areas shower unique properties consistent with the sampling used for their respective baseline values but also useful in terms of obtaining a key for identifying polygons of marsh. As was mentioned above these will generally fall within a polygon composed of vector polylines but it can occasionally be the case where the marsh was not fully enclosed. It may be necessary to introduce a process that retains all the polygons containing marsh symbols but displaying spectral values outside those expected for that type of land cover for verification – this, however, was not the case for the samples used in the study. 51
Marsh test Sample 1 (Pasture) Mean pixel value Standard Deviation Red 201.617 8.05 Green 209.713 9.569 Blue 136.645 12.082 Marsh test Sample 2 (Mixed Forestry) Mean pixel value Standard Deviation Red 69.22 34.492 Green 103.352 33.620 Blue 86.594 19.885 Marsh test Sample 1 (Paving) Mean pixel value Standard Deviation Red 246.167 8.1 Green 252.542 5.4 Blue 206.125 8 Table 8: Marsh test sample values The first sample, taken from freshly cut pasture, produced the relatively high values for the red and green colour bands that were found in the pasture testing samples for that type of ground colour. In relation to the values for marsh they showed a high level of disparity; with the mean red colour band pixel value for marsh being half of the pasture sample, and the green value for marsh being 60% of the test sample. The disparity in the blue colour band was less but this aspect of the spectral values could be used to relate the disparities found as specific to pasture, so that an examination of neighbouring polygon could use a known marsh area (presence of symbol and expected spectral values) as a reference to set the relative differences and possibly reset the marsh values to within the values for the known polygon for that particular areas. The last suggestion will not be included in this study bit it is worth noting that an algorithm which could constantly recalibrate the relative values as it processed neighbouring polygons might produce better results than one dependant on a key set during the beginning of the processing. 52
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: Marsh Sample 1 Mean Pixel Value Sta
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
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:
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
Page 119 and 120:
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
Page 133 and 134:
from which any variance would flag
Page 135 and 136:
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
Page 149 and 150:
This trend was continued for the bl
Page 151 and 152:
The green colour band pixel count a
Page 153 and 154:
4.5 Conclusion Image segmentation i
Page 155 and 156:
5 Literature Review The goal of thi
Page 157 and 158:
shown give a more detailed picture
Page 159 and 160:
dependencies” (Kettling, P.330).
Page 161 and 162:
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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