Accurate classification of remote sensing (RS) images is a perennial topic of interest in the RS community. Recently, transfer learning, especially for fine-tuning pretrained convolutional neural networks (CNNs), has been proposed as a feasible strategy for RS scene classification. However, because the target domain (i.e., the RS images) and the source domain (e.g., ImageNet) are quite different, simply using the model pretrained on an ImageNet dataset presents some difficulties. The RS images and the pretrained models need to be properly adjusted to build a better classification system. In this study, an adaptive learning strategy for transferring a CNN-based model is proposed. First, an adaptive transform is used to adjust the original size of the RS image to a certain size, which is tailored to the input of the subsequent pretrained model. Then, an adaptive transferring model is proposed to automatically learn what knowledge from the pretrained model should be transferred to the RS scene classification model. Finally, in combination with a label smoothing approach, an adaptive label is presented to generate soft labels based on the statistics of the classification model predictions for each category, which is beneficial for learning the relationships between the target and nontarget categories of scenes. In general, the proposed methods adaptively manage the input, model, and label simultaneously, which leads to better classification performance for RS scene classification. The proposed methods are tested on three widely used datasets, and the obtained results show that the proposed methods provide competitive classification accuracy compared to the state-of-the-art methods.

中文翻译：

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通过自适应学习迁移 CNN 进行遥感场景分类


遥感 (RS) 图像的准确分类是遥感社区长期关注的话题。最近，迁移学习，特别是微调预训练卷积神经网络（CNN），已被提出作为 RS 场景分类的可行策略。然而，由于目标域（即 RS 图像）和源域（例如 ImageNet）非常不同，简单地使用在 ImageNet 数据集上预训练的模型会带来一些困难。需要对RS图像和预训练模型进行适当调整，以构建更好的分类系统。在本研究中，提出了一种用于迁移基于 CNN 的模型的自适应学习策略。首先，使用自适应变换将RS图像的原始尺寸调整为一定尺寸，该尺寸适合后续预训练模型的输入。然后，提出了一种自适应迁移模型来自动学习预训练模型中的哪些知识应该迁移到RS场景分类模型中。最后，结合标签平滑方法，提出了一种自适应标签，根据每个类别的分类模型预测的统计数据生成软标签，这有利于学习场景的目标类别和非目标类别之间的关系。一般来说，所提出的方法同时自适应地管理输入、模型和标签，从而为遥感场景分类带来更好的分类性能。所提出的方法在三个广泛使用的数据集上进行了测试，获得的结果表明，与最先进的方法相比，所提出的方法提供了有竞争力的分类精度。