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similar statistical properties” (Kittler, P.323). Kittler divides image processing into six sections; (1) the sensor and (2) data collection, (4) image preprocessing, (5) segmentation and classification and (6) image interpretation. The work undertaken in this thesis relates to part five of this system, although it will differ from the work considered by Kittler’s paper in that some image interpretation will have taken palace beforehand. Kittler has identified the first part (of the segmentation and classification step described above) as analyzing remotely sensed data in order to “identify homogeneous segments in the image” (Kittler, P.324). He introduced a method for pixel-by-pixel classification to achieve this. For this method he suggests identifying and classifying all the pixels on a pixel by pixel basis and then linking identical pixels to form connected segments. In many ways this is probably the holy grail of image interpretation as if perfected a machine could automatically identify change and update maps. Kittler also proposes that segments of pixels exhibiting similar properties could be used for this purpose. This thesis does not suppose an identical method, instead it attempts to identify the proportion of pixels corresponding to hard ground in a small area polygon, subtract buildings, roads and water polygons and attach a value for impermeable surface to the area data. Kittler suggests that a Bayesian probability formula can be used to determine the class of a segment. He suggests using what he terms “ground truth data” (Kittler, P.325) for the probability function to assign pixels to a class and determine the composition of a segment. He cites the class homogeneity of land surface covers as the means to initially segment and classify the pixels. This work can be made difficult by weather conditions and instrumental scanning errors (Note: Kittler’s paper is from a time when GPS controlled measurements were not readily available and such errors are less of a factor today, though small inconsistencies can occur, particularly at high altitude). Kittler further suggests a method for partitioning the image into segments (initially into cells of 2*2 pixels as suggested by Ketting & Landgrebe, 1976). In this way he estimates that the larger size of land cover would allow an analysis to identify neighboring pixels with similar properties. He also suggests another method for identifying segments of the image which he terms “two dimensional spatial 151
dependencies” (Kettling, P.330). This is the use of the four neighbours of a given pixel to identify the probability of them being part of the same group. In terms of this thesis Kettling’s paper suggests that an analysis of aerial imagery could potentially reveal large amounts of data about selected features (particularly if the segmentation has been completed already by vector mapping). Kettling’s paper does suggest that it is possible to identify homogenous areas on a pixel by pixel basis and is the focus of this thesis. 5.1 Spectral and image considerations for the thesis This body of work forms the basis for what will be the main argument of this thesis, that it is possible to automatically capture spatial data relating to impervious ground in Irish towns, using controlled photography and matching vector data. This requires processing which makes use of the spectral data contained in aerial imagery of the sample data, which in turn presents a number of separate problems. One of these is bidirectional reflectance, and while this is not expected to be a major consideration while developing the algorithm for the thesis it nevertheless warrants consideration. One method of calculating for this is to adjust the imagery based on either ground sampled data or imagery from a higher (possibly satellite) vantage, and is something that was considered by Sakari Tuominen and Anssi Pekkarinen in their 2004 study of forestry in southern Finland. The authors consider a method of improving the value of data being retrieved from aerial photography (in conjunction with satellite data) by reducing the presence of bidirectional reflectance. This is a problem with the way light is hitting the surface of the earth causing the spectral values if image pixels to depend on their location in the image. One approach would be to focus any study on the centre of the image, where bidirectional reflectance would not be as big an issue. However, the study was attempting to find a more effective method of correcting this using overlaying satellite images and a correcting algorithm. The reason they chose satellite imagery was that they are less affected by bidirectional reflectance and this 152
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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
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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
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2 Stepping through the Algorithm Th
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The process can also be coded into
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Aerial imagery is a form of databas
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The software required for this step
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study area from the photography and
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The areas extracted were placed int
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ands was detected the standard devi
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2.4 Confirmation The final stage of
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3 Sampling for the Baseline Image K
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Figure 3: Road area and vector data
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(The previous samples were compared
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Road test sample 2 Mean pixel value
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3.2 Water Figure 4: Typical Water A
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The purpose of this thesis is to id
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present in the target area (or its
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possible to quickly process each se
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Marsh Sample 1 Mean Pixel Value Sta
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Marsh test Sample 1 (Pasture) Mean
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form both pasture and road/ paving
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Forest Sample 1 Mean pixel value St
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Coniferous forestry test sample 1 (
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whole was not analyzed but individu
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infrared signatures to identify the
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sampled in this study. In relation
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3.6 Track Figure 9: Typical Track A
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surface area that might have been i
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(Phynn et al, 2002). In this study
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3.7 Shade Figure 10: Typical Shade
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currently available for all areas a
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trends of bordering polygons mentio
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3.8 Roof Areas Figure 12: Typical R
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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
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,
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
Page 133 and 134: 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
Page 145 and 146: The histogram for the green colour
Page 147 and 148: Figure 70: Aerial view for bog test
Page 149 and 150: This trend was continued for the bl
Page 151 and 152: 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
Page 157: shown give a more detailed picture
Page 161 and 162: 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
Page 169 and 170: ased analysis, solely spectral base
Page 171 and 172: 16*16 pixels as urban or nonurban.
Page 173 and 174: 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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