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Track test sample 1 (unpaved dirt track) Mean pixel value Standard deviation Red 218.917 11.378 Green 225.708 13.658 Blue 161.583 10.1 Track test sample 2 (compacted dirt/ gravel) Mean pixel value Standard deviation Red 178.315 8.713 Green 195.648 10.802 Blue 131.056 16.122 Track test sample 3 (paved yard) Mean pixel value Standard deviation Red 174.278 8.77 Green 200.722 6.257 Blue 171.778 7.075 Table 14: Track test sample values Taking these values for a larger area would be a difficult task but the fact that the small area polygons derived from the vector mapping cut apart the image means that greater levels of information can be derived fro the same set of values in polygons with different associated coding. The initial sampling displayed values for pasture in small area land parcels surrounding dwellings matching those of the mean outside cut pasture. From this it can be inferred that a small area polygon surrounding a dwelling which displays spectral values similar to cut pasture could potentially be gravelled and the algorithm would then run a specific analysis on the values for the blue colour band. For the third sample area, the paved yard, the values matched those expected for paved covering and again these values would indicate the high probability of hard cover (patio/ concrete etc.) when present in a small area polygon surrounding a dwelling. The identification of track has been the subject of a large amount of work which looked at pattern recognition software which might extract the network of roads based on pattern recognition and the unique spectral values for this type of feature 69
(Phynn et al, 2002). In this study all roads and tracks have been captured and the purpose of analyzing the spectral qualities of these is in an effort to train an algorithm to recognise the specific properties of hard ground/ impermeable surface area within small area polygons. The results revealed distinctive qualities thanks mostly to the high reflective quality of these types of surface for red and green colour bands. As is mentioned in other sections of this study, the initial part of the algorithm involves extracting the roads (and water, and forestry, known marsh etc.) to leave a smaller number of polygons for analysis. The next step would be the removal (classification) of areas with relatively unique spectral values such as all the pasture polygons. Following this, the urban polygons would be analyzed, having been identified by the presence of building polygons within them –these would then be classified according to the nature of the building (as this data is only available in some instances the first iteration of the loop would include all buildings). The nature of the spectral values would then be compared to the values sampled here, as the low level of standard deviation among the colour bands associated with them enables classification with a degree of certainty. This type of survey has particular benefit in flood mapping and can help the development of models factoring in runoff rates during times of high rainfall. The sample area used here is just outside an urban area and as such has a useful mix of all the possible land cover types –ranging from dirt track to paved yards to forestry to pasture to dwelling houses within small polygons. Specific searches of urban developments could expect to find more homogenous ranges within each polygon. The values taken from this sampling could serve as the baseline for one of these surveys. The user would then select known areas of the target land cover being analyzed (via a mobile GPS unit or selected from the vector mapping draped over the aerial photography). A combination of the standard expected values and the entered key values could then be used to calculate the percentages across a wide area. The fact that for each area analysis the pixel variations are confined to a small area polygon means that there is less chance of a gradual distortion biasing the results, as each separate polygon is calculated based on its own features (i.e. the values of neighbouring polygons and presence of buildings mentioned above). 70
Page 1 and 2:
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
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overlaid aerial imagery. This is du
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upper case, beginning with lower ca
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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 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: surface area that might have been i
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
Page 123 and 124: 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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