HTC+ for SAR Ship Instance Segmentation

Abstract

Existing instance segmentation models mostly pay less attention to the targeted characteristics of ships in synthetic aperture radar (SAR) images, which hinders further accuracy improvements, leading to poor segmentation performance in more complex SAR image scenes. To solve this problem, we propose a hybrid task cascade plus (HTC+) for better SAR ship instance segmentation. Aiming at the specific SAR ship task, seven techniques are proposed to ensure the excellent performance of HTC+ in more complex SAR image scenes, i.e., a multi-resolution feature extraction network (MRFEN), an enhanced feature pyramid net-work (EFPN), a semantic-guided anchor adaptive learning network (SGAALN), a context ROI extractor (CROIE), an enhanced mask interaction network (EMIN), a post-processing technique (PPT), and a hard sample mining training strategy (HSMTS). Results show that each of them offers an observable accuracy gain, and the instance segmentation performance in more complex SAR image scenes becomes better. On two public datasets SSDD and HRSID, HTC+ surpasses the other nine competitive models. It achieves 6.7% higher box AP and 5.0% higher mask AP than HTC on SSDD. These are 4.9% and 3.9% on HRSID.

Keywords

• synthetic aperture radar
• ship instance segmentation
• HTC+
• deep learning
• convolutional neural network