Haze occurs almost every year in Malaysia and is caused by smoke which originates from forest fire in Indonesia. It causes visibility to drop, therefore affecting the data acquired for this area using optical sensor such as that on board Landsat - the remote sensing satellite that have provided the longest continuous record of Earth's surface. The work presented in this thesis is meant to develop a better understanding of atmospheric effects on land classification using satellite data and method of removing them. To do so, the two main atmospheric effects dealt with here are cloud and haze. Detection of cloud and its shadow are carried out using MODIS algorithms due to allowing optimal use of its rich bands. The analysis is applied to Landsat data, in which shows a high agreement with other methods. The thesis then concerns on determining the most suitable classification scheme to be used. Maximum Likelihood (ML) is found to be a preferable classification scheme due to its simplicity, objectivity and ability to classify land covers with acceptable accuracy. The effects of haze are subsequently modelled and simulated as a summation of a weighted signal component and a weighted pure haze component. By doing so, the spectral and statistical properties of the land classes can be systematically investigated, in which showing that haze modifies the class spectral signatures, consequently causing the classification accuracy to decline. Based on the haze model, a method of removing haze from satellite data was developed and tested using both simulated and real datasets. The results show that the removal method is able clean up haze and improve classification accuracy, yet a highly non-uniform haze may hamper its performance.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:575865 |
| Date | January 2013 |
| Creators | Ahmad, Asmala |
| Publisher | University of Sheffield |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.whiterose.ac.uk/14602/ |
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