Detection and segmentation of radio frequency interference from satellite images using attention-GANs

Radio frequency interference (RFI) refers to the interference in the electromagnetic spectrum caused by undesired radio signals that share the same frequency band as the desired signal. RFI can interrupt, distort, or completely eliminate radio signals, resulting in ineffective communication or interaction with other electronic systems. RFI can have several effects on satellite imagery. It can result in stripes, smears, and other visual patterns, degrading image quality and making it harder to understand. In extreme cases, RFI can entirely obscure visual characteristics of interest. Among the different techniques for identifying RFIs viz thresholding, detection using artificial intelligence (AI) has shown higher efficiency and accuracy with minimal manual intervention. This paper proposes an RFI detection and segmentation model based on an attention-enforced Generative Adversarial Network (GAN): Attention-SegmentationGAN(SegGAN), using an image-to-image translation network: Pix2Pix. This approach turns the RFI segmentation into an image translation issue and then trains two deep convolutional neural networks (CNN), concurrently to provide a binary RFI mask image. Furthermore, we enhance the network architectures of the Pix2Pix model's generator and discriminator for superior quality detection and localization of RFI from single-channel satellite images. Here, a dataset consisting of around 1000 single-channel satellite images with synthetically generated RFI in random portions of the image was used. For model developments, Standard metrics such as Sensitivity(Recall), Specificity(Accuracy), Precision, and Dice coefficient have been utilized to evaluate the detection of noise affected locations. The proposed model showed superior segmentation performance with the baseline Pix2Pix model and state-of-the-art RFI detection approaches from satellite images.