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Title : Land Cover Classification and Analysis Using Radar and Landsat Data in North Central Ethiopia
Company : George Mason University
File Name : Tadesse.pdf
Size : 538445
Type : application/pdf
Date : 27-Jul-2014
Downloads : 9

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Authors: Haile K. Tadesse and Allan Falconer

Radar and Landsat data were used to classify land cover in north central Ethiopia. Both images were registered and resampled to 12.5 m spatial resolution. Maximum Likelihood Classifier (MLC) and C4.5 algorithm were applied. The original radar data produced low overall classification accuracy (66%). To improve this classification accuracy, de-speckling and texture measures were used for image enhancements. The de-speckling methods used in this study are Median, Lee-Sigma and Gamma-MAP. Lee-sigma, Gamma-MAP and Median de-speckling improved the overall accuracy by 15, 18 and 20% respectively. The maximum overall accuracy achieved in this study by de-speckling method is 86.4% using Median at 27*27. Urban producer accuracy improved by 58% by using Median de-speckling. All de-speckling techniques improved urban user accuracy to more than 90%. In most de-speckling cases, MLC outperformed C4.5 classifier in the overall classification accuracy. The highest overall accuracies achieved by texture are 88.8 and 90.5% when MLC and C4.5 algorithms at window size 51*51 were used respectively. This shows 22% improvement compared to the original radar data. Urban and forest producer accuracy improved by 58 and 26% respectively at window size 43*43. The overall classification accuracy of Landsat data is 93.7%. Combining Landsat and derived radar data measures improved land cover accuracy by about 5%. This study showed the importance of texture and de-speckling techniques to improve a land cover classification in radar data. Therefore, radar data can be used as an alternative to optical data in the tropics and Ethiopia for land cover classification.
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