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Xiaoping Liu, Xia Li, and Xiaohu Zhang (2009) Determining Class Proportions Within a Pixel Using a New Mixed-Label Analysis Method IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING Mahesh Pal and Giles M. Foody (2009) Feature Selection for Classification of Hyperspectral Data by SVM IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING Bingcai Zhang and Neal Olander(2000) How to get GIS Data from Imagery. ESRI user conference 2000 proceedings. Retrieved on the 2 March 2010 from: proceedings.esri.com/library/userconf/proc00/professional/papers/pap427/p427.ht m Ping Zhong and Runsheng Wang (2007) A Multiple Conditional Random Fields Ensemble Model for Urban Area Detection in Remote Sensing Optical Images. IEEE Transactions on geoscience and remote sensing, Vol. 45, No. 12 Fenglei Fan, Yunpeng Wang, Maohui Qiu and Zhishi Wang. (2009) Evaluating the Temporal and Spatial Urban Expansion Patterns of Guangzhou from 1979 to 2003 by Remote Sensing and GIS Methods. International Journal of Geographical Information Science, Vol. 23, No. 11, 1371–1388 M. E. Martin, S. D. Newman, J. D. Aber, and R. G. Congalton (1998) Determining Forest Species Composition Using High Spectral Resolution Remote Sensing Data. Remote Sens. Environ. 65:249–254 (1998) Nelson, R. F., Latty, R. S., and Mott, G. (1985), Classifying northern forests using Thematic Mapper Simulator data. Photogramm. Eng. Remote Sens. 50:607–617. 169
Shen, S. S., Badhwar, G. D., and Carnes, J. G. (1985), Separability of boreal forest species in the Lake Jennette area, Photogramm. Eng. Remote Sens. 51:1775–1783. Lathrop, R. G., Aber, J. D., Bognar, J. A., Ollinger, S. V., Casset, S., and Ellis, J. M. (1994), GIS development to support regional simulation modeling of northeastern (USA) forest Analysis (W. Michener, J. W. Brunt, and S. Stafford, Eds.), Skidmore, A. K. (1989), An expert system classifies eucalypt Taylor and Francis, London, pp. 431–451. Rik Bellens, Sidharta Gautama, Leyden Martinez-Fonte, Wilfried Philips, Jonathan Cheung-Wai Chan, and Frank Canters (2008) Improved Classification of VHR Images of Urban Areas Using Directional Morphological Profiles IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 46, NO. 10. 170
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
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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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Page 113 and 114:
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
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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 and 174: as roads and car parks are so simil
Page 175: H. van der Werff and F. van der Mee
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