Abstract
We tackle the problem of class incremental learning (CIL) in the realm of landcover classification from optical remote sensing (RS) images in this paper. The paradigm of CIL has recently gained much prominence given the fact that data are generally obtained in a sequential manner for real-world phenomenon. However, CIL has not been extensively considered yet in the domain of RS irrespective of the fact that the satellites tend to discover new classes at different geographical locations temporally. With this motivation, we propose a novel CIL framework inspired by the recent success of replay-memory based approaches and tackling two of their shortcomings. In order to reduce the effect of catastrophic forgetting of the old classes when a new stream arrives, we learn a curriculum of the new classes based on their similarity with the old classes. This is found to limit the degree of forgetting substantially. Next while constructing the replay memory, instead of randomly selecting samples from the old streams, we propose a sample selection strategy which ensures the selection of highly confident samples so as to reduce the effects of noise. We observe a sharp improvement in the CIL performance with the proposed components. Experimental results on the benchmark NWPU-RESISC45, PatternNet, and EuroSAT datasets confirm that our method offers improved stability-plasticity trade-off than the literature.
Abstract (translated)
本文从光学遥感(RS)图像领域探讨了 class 增量学习(CIL)的问题。由于在实际场景中,数据通常需要以顺序方式获取,因此 CIL 已经成为一种非常热门的范式。然而,尽管卫星通常会在不同的地理区域和时间发现新类,但 RS 领域目前尚未广泛地考虑 CIL,即使考虑到卫星发现新类时可能存在的时间差异。因此,我们提出了一种基于最近成功回放记忆方法的新 CIL 框架,并解决了其两个缺点。为了在一个新的流到达时减少旧类灾难性遗忘的影响,我们学习了新的类的课程大纲,该大纲基于旧类之间的相似性。我们发现这极大地限制了遗忘的程度。在构建回放记忆时,我们而不是随机选择旧类中的样本,我们提出了一种样本选择策略,以确保选择高度自信的样本,以减少噪声的影响。我们观察到,与所选组件一起,CIL 性能得到了显著的改善。在基准数据集NWPU-RESISC45、模式Net和欧洲卫星数据集上进行了实验,实验结果显示,我们的方法比文献提供更好的稳定性和灵活性权衡。
URL
https://arxiv.org/abs/2309.01050
