University of Leicester
2016SPIESBFPhD.pdf (28.96 MB)

Land Cover Classification Using Multi-Frequency SAR over Semi-Arid and Forested African Landscapes

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posted on 2016-11-29, 12:20 authored by Bernardus Francois Spies
The potential of using multi-frequency Synthetic Aperture Radar (SAR) for land cover classification is becoming a reality, with multiple SAR satellites utilising different frequencies currently in orbit and more missions planned for the future. This study looks at combining SAR frequencies from L-band (ALOS PALSAR), C-band (ENVISAT ASAR) and X-band (TerraSAR-X) to find the optimum combination of the SAR data for land cover classification of forested and semi-arid ecoregions in Africa. The study site for forested areas is in Cameroon and the semi-arid study site is in Tanzania. Data from both the wet and dry seasons are available. Random forest models, with different combinations of input variables, are compared. Models with the top 30 variables are chosen from the mean decrease accuracy and mean decrease Gini variable importance measures, and compared with the classification accuracies using support vector machines. Some of the findings are that L-band is the best single frequency for land cover classifications for both ecoregions, with X-band the best single frequency if only forested regions are considered. Texture measures lead to an increase of between 15-25% overall classification accuracy compared to using only backscatter coefficients. The recommended dual-frequency combination are LX-bands, although L-band data give overall classification accuracies very close to LX-bands. The use of images from LCX data only marginally improves the classification accuracy from LX-images and L-band images. The benefit from acquisition of all three frequencies would therefore rarely outweigh the cost of acquiring and processing data from all three frequencies. The transferability of the random forest models to an additional geographic site did not produce satisfactory results, however the transferability of the random forest models to additional season data did give satisfactory results. The Kullback-Leibler divergence class difference measure showed potential to give an indication of transferability of the models, although refinement remains necessary.



Balzter, Heiko

Date of award


Author affiliation

Department of Geography

Awarding institution

University of Leicester

Qualification level

  • Doctoral

Qualification name

  • PhD



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