In this article, we address class incremental learning (IL) in remote sensing image analysis. Since remote sensing images are acquired continuously over time by Earth's observation sensors, the land-cover/land-use classes on the ground are likely to be found in a gradational manner. This process restricts the deployment of stand-alone classification approaches, which are trained for all the classes together in one iteration. Therefore, for every new set of categories discovered, the entire network consisting of old and new classes requires retraining. This procedure is often impractical, considering vast volumes of data, limited resources, and the complexity of learning models. In this respect, we propose a convolutional-neural-network-based framework (called CILEA-NET, curriculum-based incremental learning framework for remote sensing image classification) to efficiently resolve the difficulties associated with incremental learning paradigm. The framework includes new classes in the already trained model to avoid catastrophic forgetting for the old while ensuring improved generalization for the newly added classes. To manage the IL's stability-plasticity dilemma, we introduce a novel curriculum learning-based approach where the order of the new classes is devised based on their similarity to the already trained classes. We then perform the training in that given order. We notice that the curriculum learning setup distinctly enhances the training time for the new classes. Experimental results on several optical datasets: PatternNet and NWPU-RESISC45, and a hyperspectral dataset: Indian Pines, validate the robustness of our technique.

中文翻译：

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CILEA-NET：基于课程的遥感图像分类增量学习框架


在本文中，我们将讨论遥感图像分析中的增量学习 (IL) 类。由于地球观测传感器随着时间的推移连续获取遥感图像，因此地面上的土地覆盖/土地利用类别可能会以渐进的方式找到。这一过程限制了独立分类方法的部署，这些方法在一次迭代中针对所有类别进行训练。因此，对于发现的每组新类别，由新旧类别组成的整个网络都需要重新训练。考虑到大量的数据、有限的资源以及学习模型的复杂性，这个过程通常是不切实际的。在这方面，我们提出了一种基于卷积神经网络的框架（称为CILEA-NET，基于课程的遥感图像分类增量学习框架），以有效解决与增量学习范式相关的困难。该框架在已经训练的模型中包含新类，以避免旧类的灾难性遗忘，同时确保新添加类的泛化能力得到提高。为了解决 IL 的稳定性-可塑性困境，我们引入了一种新颖的基于课程学习的方法，其中新课程的顺序是根据它们与已训练课程的相似性来设计的。然后我们按照给定的顺序进行训练。我们注意到课程学习设置明显延长了新课程的培训时间。几个光学数据集：PatternNet 和 NWPU-RESISC45 以及高光谱数据集：Indian Pines 的实验结果验证了我们技术的稳健性。