Abstract
Fusion of Synthetic aperture radar (SAR) and optical images is a significant topic in the field of remote sensing. As a typical category of image fusion methods, pixel level image fusion algorithms have been widely used in SAR-optical image fusion to integrate their complementary information and facilitate the subsequent interpretation and application. The effectiveness of these methods has been demonstrated in different literatures based on the experiment carried on specific, individual datasets, which make a comprehensive comparison of these algorithms difficult to achieve. This paper builds a sub-meter SAR and optical image dataset covering different types of scenes, the performance of 11 pixel level image methods is then investigated based on qualitative and quantitative analysis. Result shows the gradient pyramid (GP) achieve a high quality fusion when dealing with Optical-SAR image fusion task of residents, the non subsampled contourlet transform (NSCT) performs best when fusing images containing farmland and mountains.
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