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If the sample does not match the above criteria but had a mean pixel value for the red and green colour bands close to half that of roads, and displayed a level of standard deviation three times that of roads and the red and green values represent close to double the value of those in the water polygon (taken from the image key) then the polygon is coded as rough pasture. If the sample has red values around 30% lower than mixed forestry, and 20% lower in green for the same known polygon type and the standard deviation remains within 10% of the mixed forestry then the polygon can be coded as marsh (usually a symbol under the level is present in within the polygon in the vector dataset, but not always). If the sample does not match those criteria outlined so far but has a low standard deviation across all three colour bands and contains a decrease in the mean value of over 30% for all colour bands when compared to the known road values then the sample is tested for area size, if it is above the maximum value for pasture then it is coded as bog. The remaining polygons after this step in the algorithm fall into two categories – those surrounding buildings or with mixed use and those with a high level of shade present. Further analysis is required to step through the remainder to identify areas with a homogenous pixel value but have a higher level of standard deviation due to levels of shade, and those of mixed use. The output from this part of the process are six further area sets; pasture, rough pasture, marsh and bog, areas with high standard deviation for further analysis and areas containing building polygons (automatically flagged through the vector data). 31
2.4 Confirmation The final stage of the algorithm involves the reduced data set being stepped through for manual confirmation (or compared against an additional set of values determined by the user): This part of the algorithm is concerned with tidying up some of the remaining data from the previous sweeps through the polygon. To begin with the polygon set classified as pasture is selected and analyzed for differences in the mean values of the red and green colour bands. Those with mean values above 190 on the converted greyscale in red, and 200 on the scale in green are classified as cut pasture (while initially this may not appear to be of direct value to the user, it could help with any subsequent analysis of pasture in particular). The next loop is designed to remove polygons containing homogenous pixel values whose standard deviation has been biased by a high proportion of shade in the sample. It involves checking the histogram for two peaks (one for shade and one for pasture) in the pixel count. If present, the polygons are assigned to the pasture polygon set. The process was completed using the geomatica software to extract the statistical data from the polygons of raster imagery (extracted earlier in the process using a combination of ASCII data from the vector manipulation software and the Mirone clipping function). The remaining areas were cross checked with the polygons containing buildings other than those coded s dwellings. Those found not containing a building polygon are retained for further analysis and logged according to adjacent polygon types (E.g. 123445.34 232234.34 etc. –neighbouring road, building polygon, pasture –area 7658m2). This was completed manually for the study using the Radius software and GeoTiff referencing but would be best completed inside a routine for larger samples. These unknown polygons can then be visually referenced by a user and manually categorized (displayed according to an input co-ordinate set returned from this algorithm through software such as Mirone). The result of this sample image study 32
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: ands was detected the standard devi
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
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
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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
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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
Page 153 and 154:
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
Page 159 and 160:
dependencies” (Kettling, P.330).
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identifying coffee plantations from
Page 163 and 164:
apply an algorithm to colour the da
Page 165 and 166:
In 2002 S. Phinn, M. Stanford, P. S
Page 167 and 168:
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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