3WM-AugNet: A Feature Augmentation Network for Remote Sensing Ship Detection Based on Three-Way Decisions and Multigranularity

Li Ying, Duoqian Miao, Zhifei Zhang

Abstract: With the continuous advancement of remote sensing (RS) technology, RS ship detection plays a crucial role in ensuring maritime safety and the oceanic economy, but it also faces various challenges. Most existing RS ship detection methods typically apply deblurring processing to all input images before using a feature pyramid network (FPN) to detect ships of different sizes. However, this indiscriminate operation may cause image quality degradation due to excessive deblurring. Moreover, FPN has limitations in fully utilizing multigranularity features, which is particularly severe in RS ship detection tasks. These issues severely affect the accuracy of RS ship detection. To address these problems, this article proposes an effective feature augmentation network, 3WM-AugNet, based on the three-way decisions (3WDs) and multigranularity feature learning for RS ship detection. It consists of two modules: a blurred classification and deblurring module (BCDM) and a multigranularity feature augmentation module (MFAM). BCDM aims to combine 3WD and support vector machine (SVM) to design an image clarity classification algorithm and use the multi-temporal recurrent neural network (MT-RNN) algorithm to process the blurry images classified, effectively avoiding excessive deblurring of clear images. MFAM is used to enhance the richness and robustness of feature representations for ships of different sizes by introducing the bottom-up feature fusion layer and designing an adaptive coordinate attention module. Experimental results on three commonly used datasets, FGSD2021, HRSC2016, and UCAS-AOD, show that our proposed 3WM-AugNet achieves state-of-the-art performance in RS ship detection.