View/Open - ARAN

More documents

Recommendations

Info

these two fields will by necessity start to merge. This study looks at one small aspect of mapping and how automated software could be used to increase the amount of information available to a user. The premise of the study is that, given enough previously captured and accurate data, it is possible to automatically read the landscape. In short I hope to take point and line data, slice sections from aerial photography, and run a spectral analysis. The focus of the study is in the methodology so that a means for completing automated updating is identified. I should point out that by updating I am referring to providing information relating to the percentages of ground cover within a small area polygon. The task of physically capturing new structures, roads, height values (even when considering lidar) is probably something that will always require a human eye to interpret the data to some degree, for example, if the structure is temporary or if road works are underway. It might be useful at this point to introduce some more background to this study. The island of Ireland was fully digitally mapped in 2005 and recently a new database for this data has been introduced which allows small area polygons to retain unique identifiers linked to the surrounding geometry and features. The mapping is on an update cycle but for the most part it can be assumed that these polygons will remain constant (with the percentage change even lower following the building boom of the last decade). This opens up the opportunity for someone to visit the sections of surface area represented by the area polygons over successive runs of aerial photography and extract land use change data. I mentioned earlier that the focus of the study is on establishing a methodology; this was because the motivation for assessing land use change would vary according to the user. An example of this might be someone considering the potential for flooding within an area. This person might want to take a look at the water courses and new housing developments to determine the amount of impermeable surface area (paving, patios etc.) over the course of their study. To physically do this, either using a photogrammetry tool such as SOCKET SET or field GPS, would be an onerous task. The aim of this thesis is to provide a method for automatically doing this. It might seem an obvious point, but the more information available to a process when commencing an examination of an area, 9
the higher the chances of successful data being returned. This is where this study differs from previous attempts at automated data capture. The process being suggested takes a large amount of previously captured and verified data to aid the algorithm. By this I mean most sections of the aerial imagery are extracted based on definite boundaries such as walls, streams, buildings etc. Internal polygons within the target area such as water, buildings, roads, forestry are also identified and used to aid the search. In this way the study is entirely dependent on previously surveyed data. This is something that has not been attempted before with Irish data. I did not find any similar study from overseas over the course of my research. I hope to prove to you that this is something that is possible to do and implement, using sample software. The software used in the study comes from several open source projects, and also from one commercial vector data manipulation package (Radius). These are all packages which could be considered to be generic tools. It is important to note that this is not in reference to their capabilities or any slight on the people who develop them but in that the functions being accessed are common to several similar software packages. For example, the ASCII coordinate files created using Radius could equally have been achieved using Arc<strong>View</strong> or Microstation (among others). The intention was to keep the algorithm as flexible as possible so that users could adapt it to their available resources. Some of the primary software tools being used in this study come from the GDAL (geospatial data abstraction) library. In particular, its facility for writing raster geospatial data format is used to manipulate the geoTiff files containing the aerial imagery being used in the study. GDAL came about as a project sponsored by the open source geospatial foundation, which is a non profit, non-governmental organization set up to support the development of open source geospatial software. The foundation also supports projects like geotools, grassGis, mapbender and mapgrade open source mapserver among others. In this study the GDAL library is accessed using another open source software library known as <strong>Open</strong>EV. This allows the GDAL library to be presented within an application for displaying and analyzing the data. As with GDAL, it is implemented in C but has the potential for manipulation with Python. In this study the processes will be run on a Windows 10
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: 1.2 General Introduction and Backgr
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 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 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
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 111 and 112:
Page 113 and 114:
The peak for the green colour band,
Page 115 and 116:
Page 117 and 118:
Figure 31: Vector data for rough pa
Page 119 and 120:
Figure 34: Green colour band for ro
Page 121 and 122:
Page 123 and 124:
The peak for the green colour band,
Page 125 and 126:
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 and 176:
H. van der Werff and F. van der Mee
Page 177:
Shen, S. S., Badhwar, G. D., and Ca
show all

View/Open - ARAN

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?