IcGAN4ColSAR: A Novel Multispectral Conditional Generative Adversarial Network Approach for SAR Image Colorization

SAR colorization aims to enrich gray-scale SAR images with color while ensuring the preservation of original radiometric and spatial details. However, researchers often limit themselves to using only the red, green, and blue bands of a multispectral image as the source of color information, coupled with a single-polarization channel from the SAR image. This approach neglects the intrinsic characteristics of remote sensing data and thus fails to fully leverage available information. To overcome this limitation, this research attempts to explore inclusion of all available bands from multispectral images along with dual-polarization channels from SAR imagery in the colorization process. Furthermore, we present a new colorization method called improved conditional generative adversarial network for SAR colorization (IcGAN4ColSAR). This method tries to include the spectral angle mapper index within its loss function. Sufficient experiments show that our explorations in the number of data channels and the loss function are helpful in improving the colorization performance of the SAR image.