SARGAN: A Novel SAR Image Generation Method for SAR Ship Detection Task

Abstract

Deep learning-based synthetic aperture radar (SAR) ship detection methods are significant in signal processing and radar imaging. However, these approaches always require large-scale SAR ship images with labels to train the model. Due to the inaccessibility of SAR sensors, it is difficult to acquire enough SAR images. Annotating ship targets also demands resources and manpower. To tackle this issue, we propose a novel SAR image generation method named SARGAN for SAR ship detection task. Given the position and category, SARGAN can generate realistic SAR images with SAR ship targets, land, and background in the desired location. In the SARGAN, there are five components: target encoder, scene constructor, SAR image generator, and target and image discriminators. The target encoder is introduced to predict the latent vector for each target, while the scene constructor integrates all targets in the entire scene using convolutional LSTM. We improve the structure of the SAR image generator by adding operations to generate high-quality images. The image and target discriminators are responsible for distinguishing between real and fake samples, with the latter also predicting the category. To promote the generation of diverse and realistic SAR ship images, multiple loss functions are employed for training. Additionally, we have annotated the lands and background in the high-resolution SAR images dataset (HRSID) and combined them with labeled ships to create a new dataset for training and testing of SARGAN. Extensive experiments demonstrate that SARGAN outperforms other SAR image generation methods, and the generated SAR ship images are highly conducive for SAR ship detection task.

Topics & Concepts