Power Transformations and Feature Alignment Guided Network for SAR Ship Detection
Man Xiao, Zhi He, Xinyuan Li, Anjun Lou
Abstract
Due to the capacity of full-time and full-weather working, synthetic aperture radar (SAR) images have been frequently applied to ship detection. However, the interference of speckle noise and shores poses enormous challenges to the accuracy of detection. Extracting multi-scale features is regarded as a good way to detect ships of different sizes, but features at different scales are not strictly aligned, which may further affect the detection results. Therefore, this letter proposes an anchor-free method, namely power transformations and feature alignment guided network (Pow-FAN), to solve the above problems. In Pow-FAN, we first utilize a power-based convolution block called PCB to extract features, which can suppress speckle noise and shores and enhance the ship targets. Furthermore, a novel feature alignment block named FAB is put forward to avoid the dislocation problems when integrating features of different scales. Compared with other state-of-the-art methods, Pow-FAN can achieve competitive detection results on the SAR ship detection dataset (SSDD) and the High-Resolution SAR Images Dataset (HRSID). The ablation study and discussion on the backbone and different situations further demonstrate the superiority of our network structure.