Fusion of SAR and Multispectral Images Using Random Forest Regression for Change Detection

In order to overcome the insufficiency of single remote sensing data in change detection, synthetic aperture radar (SAR) and optical image data can be used together for supplementation. However, conventional image fusion methods fail to address the differences in imaging mechanisms and cannot overcome some practical limitations such as usage in change detection or temporal requirement of the optical image. This study proposes a new method to fuse SAR and optical images, which is expected to be visually helpful and minimize the differences between two imaging mechanisms. The algorithm performs the fusion by establishing relationships between SAR and multispectral (MS) images by using a random forest (RF) regression, which creates a fused SAR image containing the surface roughness characteristics of the SAR image and the spectral characteristics of the MS image. The fused SAR image is evaluated by comparing it to those obtained using conventional image fusion methods and the proposed method shows that the spectral qualities and spatial qualities are improved significantly. Furthermore, for verification, other ensemble approaches such as stochastic gradient boosting regression and adaptive boosting regression are compared and overall it is confirmed that the performance of RF regression is superior. Then, change detection between the fused SAR and MS images is performed and compared with the results of change detection between MS images and between SAR images and the result using fused SAR images is similar to the result with MS images and is improved when compared to the result between SAR images. Lastly, the proposed method is confirmed to be applicable to change detection.