Since the current remote sensing pre-trained models trained on optical images are not as effective when applied to SAR image tasks, it is crucial to create sensor-specific SAR models with generalized feature representations and to demonstrate with evidence the limitations of optical pre-trained models in downstream SAR tasks. The following aspects are the focus of this study: pre-training, fine-tuning, and explaining. First, we collect the current large-scale open-source SAR scene image classification datasets to pre-train a series of deep neural networks, including convolutional neural networks (CNNs) and vision transformers (ViT). A novel dynamic range adaptive enhancement method and a mini-batch class-balanced loss are proposed to tackle the challenges in SAR scene image classification. Second, the pre-trained models are transferred to various SAR downstream tasks compared with optical ones. Lastly, we propose a novel knowledge point interpretation method to reveal the benefits of the SAR pre-trained model with comprehensive and quantifiable explanations. This study is reproducible using open-source code and datasets, demonstrates generalization through extensive experiments on a variety of tasks, and is interpretable through qualitative and quantitative analyses. The codes and models are open source.

中文翻译：

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SAR-HUB：预训练、微调和解释

由于当前在光学图像上训练的遥感预训练模型在应用于 SAR 图像任务时效果不佳，因此创建具有广义特征表示的特定于传感器的 SAR 模型并用证据证明光学预训练的局限性至关重要下游 SAR 任务中的模型。以下几个方面是本研究的重点：预训练、微调和解释。首先，我们收集当前大规模开源的SAR场景图像分类数据集来预训练一系列深度神经网络，包括卷积神经网络（CNN）和视觉变换器（ViT）。提出了一种新颖的动态范围自适应增强方法和小批量类平衡损失来解决 SAR 场景图像分类的挑战。其次，与光学模型相比，预训练模型被转移到各种 SAR 下游任务。最后，我们提出了一种新颖的知识点解释方法，以全面且可量化的解释来揭示 SAR 预训练模型的好处。这项研究可以使用开源代码和数据集进行重现，通过对各种任务的广泛实验证明了概括性，并且可以通过定性和定量分析进行解释。代码和模型都是开源的。