SAFE：一种基于自监督学习和掩码孪生ViTs的SAR特征提取器
SAFE: a SAR Feature Extractor based on self-supervised learning and masked Siamese ViTs
AI总结：
本文提出基于掩码孪生视觉Transformer的自监督SAR特征提取器SAFE，利用对比学习和SAR专用增强技术，在少样本分类与分割等任务中达到或超越现有方法。
摘要：
由于具备全天时、全天候工作能力，合成孔径雷达（Synthetic Aperture Radar, SAR）图像对于灾害管理、地球监测、变化检测和目标识别等多种应用至关重要。然而，有标注SAR数据的稀缺限制了大多数深度学习算法的性能。为解决这一问题，我们提出了一种新颖的基于掩码孪生视觉Transformer（masked Siamese Vision Transformers）的自监督学习框架，用于构建一个通用SAR特征提取器，命名为SAFE。我们的方法利用对比学习原理在无标注SAR数据上训练模型，提取鲁棒且可泛化的特征。SAFE适用于多种SAR获取模式和分辨率。我们引入了针对SAR图像的定制数据增强技术，例如子孔径分解和去斑处理。在包括少样本分类、分割、可视化和模式检测在内的多种下游任务上的综合评估证明了所提方法的有效性和多功能性。我们的网络在少样本分类和分割任务中达到或超越了其他最先进方法，即使未在用于评估的传感器数据上进行训练。
英文摘要：
Due to its all-weather and day-and-night capabilities, Synthetic Aperture Radar imagery is essential for various applications such as disaster management, earth monitoring, change detection and target recognition. However, the scarcity of labeled SAR data limits the performance of most deep learning algorithms. To address this issue, we propose a novel self-supervised learning framework based on masked Siamese Vision Transformers to create a General SAR Feature Extractor coined SAFE. Our method leverages contrastive learning principles to train a model on unlabeled SAR data, extracting robust and generalizable features. SAFE is applicable across multiple SAR acquisition modes and resolutions. We introduce tailored data augmentation techniques specific to SAR imagery, such as sub-aperture decomposition and despeckling. Comprehensive evaluations on various downstream tasks, including few-shot classification, segmentation, visualization, and pattern detection, demonstrate the effectiveness and versatility of the proposed approach. Our network competes with or surpasses other state-of-the-art methods in few-shot classification and segmentation tasks, even without being trained on the sensors used for the evaluation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
