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Mixed Forestry comparative sample 1 (bog) 63 Mean Pixel Value Standard Deviation Red 110.761 6.429 Green 121.958 8.253 Blue 103.929 11.605 Mixed Forestry comparative sample 2 (pasture) Mean Pixel Value Standard Deviation Red 128.801 10.469 Green 165.843 9.496 Blue 100.921 12.367 Mixed Forestry comparative sample 3 (cut pasture) Mean Pixel Value Standard Deviation Red 220.074 7.623 Green 209.855 9.249 Blue 137.92 11.493 Table 12: Mixed forestry test sample values The mixed forestry part of the sample was compared against three sample areas to add to the proportional deviations in the algorithm. The aim is to achieve a high enough level of relative values in the image to establish the composition of every polygon. The polygon extraction is made difficult by the fact that cropped small area polygons have irregular shape –the analysis of which was done against a blank background –resulting in altered standard deviation values for these samples. In the case of the three sample areas for this type of forestry the samples were rectangular areas within the uniform types used for the comparison. The first sample type used for comparison was an area of bog. This was chosen as it often occurs close to areas of mixed forestry and rough pasture (which by nature of the terrain have not been turned over to pasture). The values contained in the sample were similar (relatively) to coniferous forestry, but differed in the red colour band with almost a 35% higher mean and a notable low level of standard deviation. This low level of standard deviation was found in all three of the colour bands, and could be used to differentiate between the two types of forestry
sampled in this study. In relation to this, a sliding scale of standard deviation for pixels in the red colour band between bog, coniferous forestry and mixed forestry is evident –from under 10 values for the area of bog, averaging at 20 for areas of coniferous forestry and over 30 for areas of mixed forestry. Matching these to the mean could give a useful relative indication for automatic identification of bog present in an area. At this point it is probably useful to comment on the nature of the vector data for areas of bog. These types of areas are generally bounded by polylines (though these are not coded), it could be the case that the same polygon contains an area of bog and rough pasture (similar to mixed forestry); being able to estimate the relative values for this transitional analysis based on the above data is useful in such cases. If an area polygon bordering one or more areas containing pixel values contains values close to those of mixed forestry but with a low standard deviation it is probable that the polygon is describing this type of transitional area. The second type was a pasture sample taken for comparison as it is the most likely neighbouring polygon to an area of mixed forestry and is common to most (rural/ semi urban) areas in the country. This sample of pasture is separate from others used in this thesis but returned similar values (as expected). The most notable difference was in the level of standard deviation present in the sample area, with particular respect to the red and green colour bands. The mean pixel values for these colour bands also showed a distinct relative difference (over 45% and over 35% for the red and green bands respectively). This variation in values is a useful indication of which type of ground colour pixel values belong to. In particular it could serve as one of the primary steps in the algorithm. It is useful to begin the analysis by eliminating known values from the search so as the target comparison list (ground cover types to pixel values) is smaller and has a higher chance of being successful. One further aspect that can be included in any automated analysis looking for areas of bog is the size of the polygons in the search. Most large polygons will be assigned a value based on the input vector coding. They will generally represent known parts of the image such as plots of forestry and water parcels. The remaining large polygons will (in the context of the Irish landscape being analyzed) most probably by areas of bog. This can further be refined by eliminating areas of flat rock as islands within the large polygons (these 64
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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
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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
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of a control to calibrate most of t
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1.2 General Introduction and Backgr
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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: infrared signatures to identify the
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 113 and 114: The peak for the green colour band,
Page 117 and 118: Figure 31: Vector data for rough pa
Page 119 and 120: Figure 34: Green colour band for ro
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Figure 37: Green colour band for ro
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The peak for the green colour band,
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Figure 44: Green colour band for ro
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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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