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6 References Geospatial Data Abstraction Library (2010) GDAL utility programs. Retrieved on 18th August 2010 from: http://www.gdal.org/gdal_utilities.html Universedade do Algarve (2010) MIRONE. Retrieved on 8th July 2010 from: http://w3.ualg.pt/~jluis/mirone/ PCI Geomatics (2010) Geomatica. Retrieved on 5th June 2010 from: http://www.pcigeomatics.com/index.php?option=com_content&view=article&id= 5&Itemid=4 <strong>Open</strong>EV (2006) Geospatial Toolkit. Retrieved on 5th June 2010 from: http://openev.sourceforge.net/ Josef Kittler (1983) Image processing for remote sensing. Philosophical Times, 309, 323-335. Thomas Knudsen (2005) Pseudo natural colour aerial imagery for urban and suburban mapping. Int. Journal of Remote Sensing, Vol. 26, No.12, 2689-2698 Roman Dudarev (2009) Plain Polygon Signature Point Definition Algorithm. Survey and Land Information Science 69, No2. S. Cordero-Sancho & S.A.Ader (2007) Spectral analysis and classification accuracy of coffee crops using Landsat and a topographic-environmental model International Journal of Remote Sensing Vol. 28, No. 7, 10 April 2007, 1577–1593 167
H. van der Werff and F. van der Meer (2008) Shape-based classification of spectrally identical objects. ISPRS Journal of Photogrammetry & Remote Sensing 63, 251-258 S. Phinn, M. Stanford, P. Scarth, A. Murray and P. Shyy (2002) Monitoring the composition of urban environments based on the vegetation–impervious surface– soil (VIS) model by sub pixel analysis techniques. International Journal of Remote Sensing, vol. 23, no. 20, 4131–4153 Sakari Tuominen and Anssi Pekkarinen (2004) Local radiometric correction of digital aerial photographs for multi source forest inventory. Remote Sensing of Environment 89, 72–82 Manuel A. Aguilar, Fernando J. Aguilar and Francisco Aguilera (2005) Mapping small areas using a low-cost close range Photogrammetric package with aerial photography. The Photogrammetric Record 20(112): 335–350 Yuyu Zhou' and Y.Q. Wang' (2007) An Assessment of Impervious Surface Areas in Rhode Island NORTHEASTERN NATURAUST I4 {4):643-650 Megan M. Lewis (1998) Numeric classification as an aid to spectral mapping of vegetation communities. Plant Ecology 136: 133–149 Yong Kui Liua, Xiao Qiang Wanga, Shu Zhe Baoa, Matej Gombosib, Borut Zalik (2007) An algorithm for polygon clipping, and for determining polygon intersections and unions Computers & Geosciences 33 (2007) 589–598 Rama Rao Nidamanuri Bernd Zbell (2010) A method for selecting optimal spectral resolution and comparison metric for material mapping by spectral library search Progress in Physical Geography 34(1) 47–58 168
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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
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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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Figure 37: Green colour band for ro
Page 123 and 124: The peak for the green colour band,
Page 125 and 126: Figure 44: Green colour band for ro
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 and 158: shown give a more detailed picture
Page 159 and 160: dependencies” (Kettling, P.330).
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: as roads and car parks are so simil
Page 177: Shen, S. S., Badhwar, G. D., and Ca
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