High-Resolution Feature Generator for Small-Ship Detection in Optical Remote Sensing Images

Ship detection in optical remote sensing (RS) images remains a persistent challenge in current research. While prevailing methods achieve satisfactory outcomes in detecting large ship objects within RS images, the identification of small-ship objects poses greater difficulty due to their limited pixel information. To address this challenge, the utilization of generative adversarial network-based (GAN-based) super-resolution (SR) techniques proves effective. Therefore, in this article, we present a high-resolution feature generator (HRFG) specifically tailored for small-ship detection. Different from previous GAN-based methods that rely on image-level SR or feature sharing between SR and detection, we design a new architecture that uses an additional network branch, that is, high-resolution feature extractor (HRFE), to extract real high-resolution (HR) feature as a feature-level supervisory signal. The intuition is that real HR features may guide the generator network to extract HR features from low-resolution (LR) images directly. Consequently, the feature for detection is extracted and enhanced at the same time so that a large amount of calculation brought by image-level SR is avoided. Additionally, we introduce a background degradation strategy within the HRFE to improve the performance of small object recognition. Extensive experiments on a self-assembled ship dataset and two other public datasets show the superiority of the proposed method in small-ship detection tasks.