Political, environmental, and commercial needs for information on the Earths surface and atmosphere drive the development of improved satellite data products. At visible and thermal wavelengths the quality of these products is dependent on our ability to distinguish between clouds and the underlying surface. Unlike oceans, land surfaces are highly heterogeneous, containing a wide range of materials, some of which exhibit similar spectral properties to cloud, and hence it is much harder to distinguish between the two. ~ This research project, supported by the Along-Track Scanning Radiometer (ATSR) science team at the Rutherford Appleton Laboratory (RAL), addresses the need for improved cloud detection over land surfaces through the development of an unsupervised cloud detection system for global ATSR-2 scenes over land surfaces. The thesis details the development of the first successful un-supervised near-global cloud detection scheme for ATSR-2 scenes over land surfaces. The scheme developed operates on ATSR-2 data using a fuzzy set methodology. The level of membership of the fuzzy sets is determined using aggregated Gaussian distribution functions defined in a knowledge base that has been developed from the International Satellite Cloud Climatology Project (ISCCP) data sets. This is the first cloud detection algorithm that is uniquely customisable to its end users needs. Specifically, this is achieved through the use of fuzzy set theory and set membership grades. This elegant solution to the problem achieves cloud detection as oppose to cloud clearing, and its final output retains all the information computed on possible classifications of image pixels, thus providing the end user with a true representation of the imprecision inherent in the real-world data. A comprehensive quantitative evaluation and inter-comparison of cloud clearing schemes is presented. This showed that with respect to other algorithms (in literature and currently under development at RAL) F-CLOUD is one of the frontrunners in a new generation of cloud detection algorithms over land surfaces. The scheme is highly accurate and has immediate potential applications within the development programme of future ATSR-2/AATSR products at RAL. Using confusion matricies to analyse hardened results yielded a mean classification accuracy of 92.3% (for a total of forty-five scenes analysed against neph-analysis derived cloud masks).
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:693809 |
| Date | January 2001 |
| Creators | Smith, R. J. |
| Contributors | Hobbs, S. E. ; Morris, Joe |
| Publisher | Cranfield University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://dspace.lib.cranfield.ac.uk/handle/1826/11011 |
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