Block-regression-based Fusion of Optical and Sar Imagery for Features Enhancement

This paper focuses on fusion of optical and Synthetic Aperture Radar (SAR) images to combine these two types of remotely sensed imagery for features enhancement. We have proposed a new fusion technique, namely Block-based Synthetic Variable Ratio (Block-SVR), which is based on block multiple linear regression to fuse optical and SAR imagery. In order to investigate the effectiveness, the fusion results of higher resolution airborne SAR image and lower resolution multispectral image have been given. According to the fusion results, the fused images have enhanced certain features, namely spatial and textural contents as well as features not visible in multispectral while preserving the color characteristics. Also the spectral, spatial and textural effects of the presented algorithm were evaluated mainly by visual and quantitative methods comparing to those of IHS, PCA and Wavelet-based methods. During the implementation of the block-regression-based techniques there are at least two advantages. One is that the block-regression-based technique drastically decreases amount of computation, however regression of the whole scene image is almost impossible. The other advantage, also the most important, is that adjustment of regressed-block size can result in different emphases between preservation of spectral characteristic and enhancement of spatial and textural contents. The larger is the block of regression, the more are the spatial and textural details enhanced. In contrast, the smaller is the block of regression, the more are spectral features preserved.