Analysis of radarsat-2 polarimetric data for forest mapping
Date
2012
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Abstract
Forests are a major natural resource of the Earth and control a wide range of environmental processes. Forests comprise a major part of the planet's plant biodiversity and have an important role in the global hydrological and biochemical cycles. Among the numerous potential applications of remote sensing in forestry, forest mapping plays a vital role for characterization of the forest in terms of species. Particularly, in Canada where forests occupy 45% of the territory, representing more than 400 million hectares of the total Canadian continental area. In this thesis, the potential of polarimetric SAR (PolSAR) Radarsat-2 data for forest mapping is investigated.
This thesis has two principle objectives. First is to propose algorithms for analyzing the PolSAR image data for forest mapping. There are a wide range of SAR parameters that can be derived fom PolSAR data. In order to make full use of the
discriminative power offered by all these parameters, two categories of methods are proposed. The methods are based on the concept of feature selection and classifier ensemble. First, a nonparametric definition of the evaluation function is proposed and
hence the methods NFS and CBFS. Second, a fast wrapper algorithm is proposed
for the evaluation function in feature selection and hence the methods FWFS and
FWCBFS. Finally, to incorporate the neighboring pixels information in classification
an extension of the FWCBFS method i.e. CCBFS is proposed. The second objective of this thesis is to provide a comparison between leaf-on (summer) and leaf-off (fall)
season images for forest mapping. Two Radarsat-2 images acquired in fine quad
polarized mode were chosen for this study. The images were collected in leaf-on and
leaf-of seasons. We also test the hypothesis whether combining the SAR parameters
obtained fom both images can provide better results than either individual datasets may be useful for forest mapping.
To assess the potential of the proposed methods their performance have been compared with each other and with the baseline classifiers. The baseline methods
include the Wishart classifier, which is a commonly used classification method in PolSAR community, as well as an SVM classifier with the full set of parameters.
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Experimental results showed a better performance of the leaf-of image compared
to that of leaf-on image for forest mapping. It is also shown that combining leaf
off parameters with leaf-on parameters can significantly improve the classification accuracy. Also, the classification results (in terms of the overall accuracy) compared
to the baseline classifiers demonstrate the effectiveness of the proposed nonparametric
scheme for forest mapping.
Description
Bibliography: p. 123-140
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Citation
Maghsoudi, Y. (2012). Analysis of radarsat-2 polarimetric data for forest mapping (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/4817