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 which rely on image-level SR or feature sharing between SR and detection, we design a new architecture that uses an additional network branch, i.e., 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 feature from low-resolution (LR) image directly. Consequently, the feature for detection is extracted and enhanced at the same time so that 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 superiority of the proposed method in small ship detection task.