School of Geosciences and Info-Physics, Central South University, Changsha 410083, China.
China Academy of Launch Vehicle Technology Research and Development Center, Beijing 100076, China.
Sensors (Basel). 2020 Feb 24;20(4):1226. doi: 10.3390/s20041226.
Remote sensing image scene classification has a high application value in the agricultural, military, as well as other fields. A large amount of remote sensing data is obtained every day. After learning the new batch data, scene classification algorithms based on deep learning face the problem of catastrophic forgetting, that is, they cannot maintain the performance of the old batch data. Therefore, it has become more and more important to ensure that the scene classification model has the ability of continual learning, that is, to learn new batch data without forgetting the performance of the old batch data. However, the existing remote sensing image scene classification datasets all use static benchmarks and lack the standard to divide the datasets into a number of sequential learning training batches, which largely limits the development of continual learning in remote sensing image scene classification. First, this study gives the criteria for training batches that have been partitioned into three continual learning scenarios, and proposes a large-scale remote sensing image scene classification database called the Continual Learning Benchmark for Remote Sensing (CLRS). The goal of CLRS is to help develop state-of-the-art continual learning algorithms in the field of remote sensing image scene classification. In addition, in this paper, a new method of constructing a large-scale remote sensing image classification database based on the target detection pretrained model is proposed, which can effectively reduce manual annotations. Finally, several mainstream continual learning methods are tested and analyzed under three continual learning scenarios, and the results can be used as a baseline for future work.
遥感图像场景分类在农业、军事等领域具有很高的应用价值。每天都会获得大量的遥感数据。在学习了新的一批数据后，基于深度学习的场景分类算法面临灾难性遗忘的问题，即无法保持旧批数据的性能。因此，确保场景分类模型具有持续学习的能力（即学习新的一批数据而不忘记旧批数据的性能）变得越来越重要。然而，现有的遥感图像场景分类数据集都使用静态基准，缺乏将数据集划分为多个连续学习训练批次的标准，这在很大程度上限制了遥感图像场景分类中持续学习的发展。首先，本研究给出了已分割为三个持续学习场景的训练批次的标准，并提出了一个名为持续学习遥感基准（CLRS）的大规模遥感图像场景分类数据库。CLRS 的目标是帮助开发遥感图像场景分类领域的最新持续学习算法。此外，在本文中，提出了一种基于目标检测预训练模型构建大规模遥感图像分类数据库的新方法，可以有效减少人工标注。最后，在三个持续学习场景下对几种主流的持续学习方法进行了测试和分析，结果可以作为未来工作的基准。
