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3.3 Marsh Figure 7: Typical Marsh Area Image This study looked at areas of marshy ground. The purpose was to try and identify if sections of waterlogged surface area had unique values which could be identified in a small area polygon. The areas used for the study were captured examples adjoining a lake –they had been field revised and identified specifically as marsh within an enclosed polygon. The boundary on one side was the edge of the lake, while the boundary on the other side was the border with pasture enclosed with a notional (mapping) line. The samples (three in total) can be assumed to be typical of marsh (due to the position within the target area) but there was a small amount of variation, around 5%, between results for the different colour bands. This was as expected and the values were very close to the image average. This suggests that areas of marsh would be difficult to detect using a mean pixel/ standard deviation analysis based on area polygons alone, and additional coding data taken from the original vector mapping is required to make an accurate prediction as to the probability of marshy ground being present. 49
Marsh Sample 1 Mean Pixel Value Standard Deviation Red 110.968 10.557 Green 135.143 12.497 Blue 102.401 13.700 Marsh Sample 2 Mean Pixel Value Standard Deviation Red 108.672 16.799 Green 132.305 15.717 Blue 93.646 14.9 Marsh Sample 3 Mean Pixel Value Standard Deviation Red 104.652 8.66 Green 123.725 10.028 Blue 94.018 13.032 Table 7: Marsh sample values One factor which might help to differentiate between areas of marsh and the overall mean is the fact that the standard deviation was less than half of the overall for the red and green colour bands in all three samples. This is due to the fact that although there is variety in the spectral values for the vegetation present; the area is uniformly covered by vegetation. This difference could become useful when removing known features from a polygon; in other words taking water and the built environment from an area polygon, and analyzing to see if the spectral values displayed similar levels of deviation on the red and green colour bands. Marsh areas are generally indicated by a symbol which signifies the presence of this type of ground cover extending to the next logical boundary (or the notional mapping boundary mentioned above). The boundaries in the vector data do not contain the level marsh so a method to identify the areas from the relatively narrow red colour band identified above could provide an automated way of determining the extent of marsh lands based on existing data. This is one of the areas of the study which does not lend itself to a software solution and relates to the question of “fuzzy data” and how to incorporate it into a digital environment. Without digressing onto a tangent outside the scope of this thesis it needs to be mentioned, in the context of this part of the study, that certain features of this 50
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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: possible to quickly process each se
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
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Figure 17: Creating the ASCII file
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Figure 20: Green colour band for pa
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The peak for the green colour band,
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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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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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