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2.2 Area Extraction The following section introduces the second part of the algorithm, where it steps into a series of loops for cutting out known areas (via vector data) from the image: The second step in the process is to extract the known areas from the study area in the imagery. This involves creating a set of polygons conforming to the coded values and excluding them from the image search. This set is then either flagged for analysis further in the algorithm (should a target area search have been created by the user in the first step) or placed in a holding set for inclusion in the statistical data output at the end of the analysis. The output from this step should be a set of unknown polygons and their associated raster image sections, along with the sets of known polygons. For this study the software employed for this step in the process was Radius (to obtain co-ordinate data for cropping the image into area polygons) and Mirone (to crop the image). This thesis was written with a view to operating on a new spatial database being developed for ordnance survey data. This database will have the capacity to return sets of values in GML form, which would mean that the input sets for this step in the algorithm would be more easily obtained by extracting … using a text editor to create a master input set. For the purposes of this study, however the input files were created in ASCII format from the polygon co-ordinate sets outlined in Radius (by copying and pasting). These co-ordinate sets were then imported into the Mirone software and used to create closed polygons, which in turn allowed the target areas to be exported. It should be noted that this is a user intensive process and was used only to test the theory being proposed in this thesis. There are many ways in which this part of the image analysis process could be automated and the process time reduced, but the focus of this study is to prove that usable data can be obtained using the methods outlines and they were not expanded on. 27
The areas extracted were placed into sets according to their nature and the total area of the sets recorded (using the area property associated with the input vectors –note these can also be calculated at the raster extraction stage using the Mirone measurement function). For each set of imagery run the image key needs to be reset to match the spectral values present, and the area sets created at this stage can then be used for this calibration. The algorithm itself is concerned with the proportional difference between the pixel values in the polygons so new values can be applied for the image key using the methods outlined during the sampling section of this thesis. This means that, should a new set of photography be used the initial sets for this stage are analyzed to create new baseline data. In other words the value for each polygon of road, section of coniferous and mixed forestry, river, lake and pond (though not reservoir as it did not return reliable man pixel value settings during the sampling); the mean pixel value and standard deviation by colour band are recorded and averaged by group. These averages in turn are tested by the expected proportional relationships between them and once verified are applied as image keys for that particular run of photography. In the case of this study this was completed using PCI geomatics geomatica software, which returned statistical data for the clipped polygons using the analysis function against the red, green and blue colour bands. As with the step as a whole, this process could benefit from an application specific to the algorithm which would return these values for the purposes of creating an image key alone. This stage of the algorithm does not require any user input (the type of photography is entered at the start of the analysis). The outputs are a series of sets of polygons and their associated raster image areas containing the image projection (in GeoTiff format). Once the input data for this stage is available in GML/ XML format then it should be possible to code a series of iterative loops to set up the sets and reduce the amount of remaining polygons required for spectral analysis. These improvements on the step being described would serve to speed up the analysis and make the process neater to the user but in order to ensure the process was worthwhile they were omitted and the focus of the study concentrated on defining relational values and determining if the vector/ raster analysis hybrid model would reveal useful data for the user. 28
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: study area from the photography and
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 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
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These mean greyscale pixel values f
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Roof test sample 1 Mean pixel value
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3.9 Pasture Figure 15: Typical Past
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From the above samples, samples 4 a
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For the track/ hard cover the diffe
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3.10 Rough Pasture Figure 16: Typic
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The identification of rough pasture
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cycle being suggested here to prese
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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
Page 111 and 112:
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The peak for the green colour band,
Page 115 and 116:
Page 117 and 118:
Figure 31: Vector data for rough pa
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Figure 34: Green colour band for ro
Page 121 and 122:
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The peak for the green colour band,
Page 125 and 126:
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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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