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1 Project Outline 1.1 Project Overview The following study is an attempt to devise an automatic method of analyzing aerial photography based on vector data. It presents an algorithm and calibration data for someone seeking to complete a search of aerial imagery based on a spectral signature. The premise on which the work was undertaken was that, given the small area polygons and coding data present in ordnance survey data, it should be possible to cut a controlled aerial photograph into a mosaic of sections and automatically identify the type of ground cover. The goal of the study was to identify a series of steps with which this could be completed. These steps are intended as a template for either a standalone application which could run searches over large geographical areas, or as a means of achieving the result for smaller areas using existing open source software libraries. This document is aimed at people seeking to develop a generic tool for completing a spectral analysis of aerial photography, or for anyone looking to execute a search of the Irish landscape for data which exhibits a distinct spectral value (crop disease, impermeable surface area, flooding etc.). The study differs from other methods of automatic image processing in that it takes existing analysis (in the form of Ordnance Survey vector data) and uses it to convert the spectral data into manageable sections. This summary presents a chronological overview of the work completed, and outlines the process used. The basis of this study is the clipping of raster data for spectral analysis, and the intention is to prove that this makes the process of image analysis easier. Traditional approaches can refine the analysis itself to reveal more about the region of interest than is completed here. For example, a lidar (aerial laser imaging) survey could provide researchers with data relating to the height of a tree canopy or the depth of peat in a bog. This study, while it does identify specific sets of values relating to land use types, focuses on identifying an easily replicated process for automatically obtaining data from aerial imagery. 1
The process was designed so that it can be coded into a standalone process and has been tested using open source software. In this way the study is aimed at simplifying what can be expensive and time consuming into a series of steps that someone without a high level of training in either mapping or computer science could run. One of the difficulties presented by attempting to find data from imagery is identifying target areas within a region of interest. This is compounded by the nature of values returned by an aerial image –the clusters of pixels with similar values are often not bounded by clean borders and often display a gradual gradient of values when merging with another cluster. In other words to automatically determine the true values on the ground a program needs to know the extent of the set of data that the clusters sit in. One analogy might be tables in a relational database –by dividing the total set of pixels for a region of interest into discrete parcels of land reflected in the photograph the program has a database of tables to query for specific values. This study takes the vector data and uses it to create a mosaic of separate pixel groups for analysis. This in itself, however, still leaves a huge body of data to be analyzed. To further improve an analysis the parcels within the mosaic are classified according to known values taken from the vector coding, and these known values are then used to train an image key, which in turn allows the remaining parcels to be analyzed. It is this clipping process which is at the core of this study. This provides a means of accessing the raster data which readers can easily replicate and automate for their own purpose. Vector data has been used to target and control aerial image analysis in previous studies with a degree of success. The studies often involve additional user input to refine the region of interest so that automated analysis techniques like multivariate analysis of variance can be applied to the image. This process of refining the region requires a level of technical expertise which could make a spectral analysis of aerial imagery too time consuming for many users. An example of one of these processes is contained in the 2007 assessment of impermeable surface area by Yuyu Zhou and Y.Q. Wang (Zhou & Wang, 2007). The authors determined that segmentation would be the most important part of the study and applied an algorithm of multiple-agent segmentation and classification. This involved importing transportation data to create buffers along the major roads at varying 2
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: Abstract This study sets out an alg
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 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
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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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The peak for the green colour band,
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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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