Disentangled Feature Representation Learning for SAR to Optical Image Translation

This work proposes a disentangled feature-based Generative Adversarial Network (GAN) for SAR-to-optical image translation. The generator consists of two encoders and a decoder. During training, one of the encoders extract shared structural features from SAR and optical images, while the other captures domain-specific style information from optical image. These structure and style features are concatenated and fed into the decoder to reconstruct the optical image. The generated and real optical images are compared by a discriminator, which provides a score for the generated image being real or fake. This score is used in the loss calculation. The network is optimized using both contrastive loss, to enforce alignment of content features across domains, and adversarial loss, to achieve realistic optical outputs. After training, the style encoder generates optical style representations that are reduced with PCA to form a representative style vector. During translation, the SAR image is encoded to obtain structural features, which are combined with the fixed style vector and decoded into the optical image. The framework enables unpaired training and style transfer. Evaluation using PSNR, SSIM, and FID confirmed high visual quality and structural consistency in the generated outputs.