Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 08/25/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspxAbstract. The contribution of dual-polarized synthetic aperture radar (SAR) to optical data for the accuracy of land use classification is investigated. For this purpose, different image fusion algorithms are implemented to achieve spatially improved images while preserving the spectral information. To compare the performance of the fusion techniques, both the microwave X-band dual-polarized TerraSAR-X data and the multispectral (MS) optical image RapidEye data are used. Our test site, Gediz Basin, covers both agricultural fields and artificial structures. Before the classification phase, four data fusion approaches: (1) adjustable SAR-MS fusion, (2) Ehlers fusion, (3) high-pass filtering, and (4) Bayesian data fusion are applied. The quality of the fused images was evaluated with statistical analyses. In this respect, several methods are performed for quality assessments. Then the classification performances of the fused images are also investigated using the support vector machines as a kernel-based method, the random forests as an ensemble learning method, the fundamental k-nearest neighbor, and the maximum likelihood classifier methods comparatively. Experiments provide promising results for the fusion of dual polarimetric SAR data and optical data in land use/cover mapping. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 08/25/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx fusion of MS LANDSAT and ERS-1 SAR images is addressed. 7 The benefit of fusion is demonstrated by the maximum likelihood classifier (MLC). In this study, the classification accuracy of vegetation did not improve when using the SAR data, but the discrimination of urban areas was enhanced. The high-resolution spotlight modes of TerraSAR-X and RapidEye are fused using principal component (PC) substitution, Ehlers fusion (EF), Gram-Schmidt (GS), highpass filtering (HPF), modified intensity hue saturation (M-IHS), and Wavelet algorithms. 8 In both visual and statistical analyses, the HPF method gave better results. Another study suggest that PC, color normalization, GS, or the University of New Brunswick methods should only be used for a single date and a single sensor dataset. 4 Ehlers method is the only one that preserved the spectral information of the MS data, which is suitable for classification. 4 Dual-polarized HH-HV (H-Horizontal, V-Vertical) RADARSAT and PALSAR data are fused with LANDSAT-TM data for the comparison of land cover classification. 2 The study indicated that among the discrete wavelet transform (DWT), HPF, principal component analysis (PCA), and normalized multiplication methods, only DWT improved the overall accuracy of the ML...