Fusing optical and synthetic aperture radar images based on shearlet transform to improve urban impervious surface extraction

Abstract

In order to extract urban impervious surfaces (ISs) accurately, optical and synthetic aperture radar (SAR) images fusion was recognized as one promising method. However, most fusion methods currently focus on feature-level and decision-level fusions. There are only a few studies exploring the performance of the fused image at the pixel level for IS extraction. Therefore, we introduced the shearlet transform to fuse Landsat-8 and TerraSAR images and evaluated the fused image by comparing it to those obtained using conventional image fusion methods. The IS from the fused images using the support vector machine algorithm is extracted and compared. Experimental results indicate some interesting findings. First, the shearlet transform can fully retain the spectral information from the optical image and the spatial information from the SAR image. Second, the IS extraction from the fused image with the shearlet transform achieved the highest accuracy with an overall accuracy of 95.1% and a Kappa coefficient of 0.8792, which confirmed the proposed method is applicable to IS extraction. We can conclude that an effective pixel-level fusion algorithm for optical and SAR images can significantly improve the extraction accuracy of urban IS. Our research could provide an innovative fusion technique and also could serve as a meaningful reference for further applications of optical and SAR imagery. In addition, the potential of SAR data in IS extraction should be further investigated.