A Fusion Method of Optical Image and SAR Image Based on Dense-UGAN and Gram–Schmidt Transformation

Abstract

To solve the problems such as obvious speckle noise and serious spectral distortion when existing fusion methods are applied to the fusion of optical and SAR images, this paper proposes a fusion method for optical and SAR images based on Dense-UGAN and Gram–Schmidt transformation. Firstly, dense connection with U-shaped network (Dense-UGAN) are used in GAN generator to deepen the network structure and obtain deeper source image information. Secondly, according to the particularity of SAR imaging mechanism, SGLCM loss for preserving SAR texture features and PSNR loss for reducing SAR speckle noise are introduced into the generator loss function. Meanwhile in order to keep more SAR image structure, SSIM loss is introduced to discriminator loss function to make the generated image retain more spatial features. In this way, the generated high-resolution image has both optical contour characteristics and SAR texture characteristics. Finally, the GS transformation of optical and generated image retains the necessary spectral properties. Experimental results show that the proposed method can well preserve the spectral information of optical images and texture information of SAR images, and also reduce the generation of speckle noise at the same time. The metrics are superior to other algorithms that currently perform well.

Keywords

• image fusion
• generative adversarial network
• loss function
• Gram–Schmidt
• remote sensing image