You also want an ePaper? Increase the reach of your titles
YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.
4.5 Conclusion<br />
Image segmentation is one of the most important parts of automatic analysis of<br />
aerial imagery (Zhou & Wang, 2007). A set of reference data is necessary to know<br />
where to divide image sections. This can be obtained from a survey input by the<br />
user or from spatial data specific to the area being studied (peat, forestry etc.).<br />
Ordnance survey vector data provides a comprehensive set of reference points and<br />
allows an aerial image to be cropped into small discrete area polygons. These<br />
polygons can also benefit from the previously captured coding which identifies<br />
many of them as a specific land type. The result of adding this data to an<br />
automatic search for specific spectral values is that the user can gain context from<br />
known neighbouring polygons and calibrate the specific search accordingly. This<br />
in turn means that the process of image analysis can be simplified by applying a<br />
generic technique for identifying polygons and refining it to search for a given<br />
value.<br />
This study looked at the value of cropping aerial imagery into a mosaic of known<br />
and unknown polygons. It attempts to automatically derive probable types for the<br />
unknown areas based on the known data and a sampled image key. The sampling<br />
and testing undertaken during the study indicated that it is possible to derive<br />
useful value from a spectral analysis based on a pixel count alone. This was<br />
because the vector data introduced into the process reduced the number of<br />
possible values that can be attributed to a pixel set –for example, as the extent of<br />
forestry is known, similar values returned from an unknown polygon must<br />
represent marsh or rough pasture while further analysis of the shape of the range<br />
can distinguish between either.<br />
The process is possible using open source software but could also be coded into a<br />
standalone application, e.g. using the GDAL library and a function to crop<br />
irregular polygons. The potential for automation will be supported by the release<br />
of the vector data in GML format, from which a large ASCII file of coordinate<br />
sets could be fed into the process. By removing the requirement for a user to<br />
control areas of the image through the use of the vector data, and by presenting the<br />
146
Change language