LAUSR

Despite the extraordinary success of the deep architectures on semantic segmentation for remote sensing (RS) images, they have difficulties in learning new classes from a sequential data stream because of catastrophic forgetting. Continual learning for semantic segmentation (CSS) is an emerging trend for its capability to cope with the above problems effectively. However, old classes from previous steps are collapsed into the background, which further aggravates the challenge of CSS in the RS scene. In this article, we revisit the knowledge distillation (KD) strategy and the characteristics of class-incremental semantic segmentation (CISS) and then present a generalized and effective framework to learn new classes while preserving knowledge of the learned classes. In particular, we propose two novel historical information-guided modules: the feature global perception module and the label reconstruction (LR) module. The former enables the current model to pay more attention to the region related to the old categories identified by the historical information when learning new classes. Meanwhile, the latter retrieves pixels belonging to the learned classes from the background to handle the background shift problem and maintain the high performance of old classes. We have conducted comprehensive experiments on two RS semantic segmentation datasets of Instance Segmentation in Aerial Images Dataset (iSAID) and Gao Fen (GF) challenge semantic segmentation dataset (GCSS). The experimental results outperform the current state-of-the-art methods in most incremental settings, which demonstrates the effectiveness of the proposed framework.