Incremental Learning of Remote Sensing Target Classification with Class Hierarchy

Yang Chu, Peng Wang, Yuntao Qian

Abstract: Incremental learning can continuously learn to address new tasks from new data while preserving knowledge learned from previously learned tasks. Incremental remote sensing target classification aims to accurately classify newly added classes while maintaining the classification performance of the old classes. In this paper, we propose a new incremental learning method using the information of class hierarchy (CH) and the strategy of Learning without Forgetting (LWF), named CH-LWF. As we know, the target classes in remote sensing images always have hierarchical relationships and are organized as hierarchical tree structures, and CH has been proven to benefit target classification. Our work in this paper may be the first to introduce CH into incremental learning of remote sensing images. In addition, LWF can learn new classes only with new training samples, and the training samples for old classes are not required to be reused, which is convenient in real applications.

External IDs:dblp:conf/igarss/ChuWQ23