Robust Self-Paced Incremental Learning for Multitemporal Remote Sensing Image Classification

Classification of multitemporal remote sensing (MTRS) images with only a few labels of one of these images has attracted widespread interest in recent years. It usually confronts three problems: domain increment, class increment, and class disappearance. In this article, a robust self-paced incremental learning (RSPIL) is proposed to alleviate the above problems. First, change detection is used to transfer labels from the source image to the target one for formulating a combined training set. Then, the weighted classification loss and the distillation loss are considered to ensure classification performance and minimal forgetting. In particular, a novel entropy-inhibit loss is proposed to suppress the classification capability for the disappearing classes. These losses are combined with self-paced learning (SPL) by introducing a weight variable to measure the "easiness" of the training samples, which is able to automatically acquire accurate decision boundaries from easy to hard since the combined training set generated by change detection contains noisy samples and outliers. Finally, a nearest-average-eigenvector classifier and an exemplar set management strategy based on the sample weights are designed to alleviate catastrophic forgetting (CF). Twenty-four MTRS image datasets from four areas are considered in the experiments. The classification results demonstrate that the proposed method is able to alleviate CF and achieves significant improvements on multitemporal image datasets.