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.

我们在这篇论文中解决了光学遥感图像的土地覆盖分类领域中的逐类增量学习（CIL）问题。我们提出了一个新颖的 CIL 框架，通过受到重播存储器方法的启发并解决了其中的两个缺点。实验证明，我们的方法在 CIL 性能上表现出明显的改进。