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.

基于遮掩的孪生视觉变压器提取器 (SAFE) 是一种新颖的自监督学习框架，通过对未标记的合成孔径雷达影像数据进行对比学习，提取稳健且具有普适性的特征。SAFE 可用于多种合成孔径雷达采集模式和分辨率下，具有针对合成孔径雷达影像的数据增强技术，如亚孔径分解和去斑点处理。对少样本分类、分割、可视化和模式检测等多种下游任务的全面评估表明，所提出的方法的有效性和多功能性。即使在未经使用的传感器上进行评估，我们的网络在少样本分类和分割任务上与或胜过其他先进方法。

SAFE：基于自监督学习和掩蔽孪生 ViTs 的 SAR 特征提取器

SAFE: a SAR Feature Extractor based on self-supervised learning and  masked Siamese ViTs

A reliable yet inexpensive tool for the estimation of flood water spread is
conducive for efficient disaster management. The application of optical and SAR
imagery in tandem provides a means of extended availability and enhanced
reliability of flood mapping. We propose a methodology to merge these two types
of imagery into a common data space and demonstrate its use in the
identification of affected populations and infrastructure for the 2022 floods
in Pakistan. The merging of optical and SAR data provides us with improved
observations in cloud-prone regions; that is then used to gain additional
insights into flood mapping applications. The use of open source datasets from
WorldPop and OSM for population and roads respectively makes the exercise
globally replicable. The integration of flood maps with spatial data on
population and infrastructure facilitates informed policy design. We have shown
that within the top five flood-affected districts in Sindh province, Pakistan,
the affected population accounts for 31 %, while the length of affected roads
measures 1410.25 km out of a total of 7537.96 km.

本文提出了一种将光学和 SAR 遥感图像融合到共同数据空间中的方法，并在巴基斯坦 2022 年洪水中进行实证研究，揭示了洪水泛滥对地区人口和基础设施的影响。

利用 Sentinel-1、Sentinel-2 和 Landsat-9 图像融合改进洪水制图以识别受洪灾影响的人口和基础设施以实现高效政策设计

Improved flood mapping for efficient policy design by fusion of  Sentinel-1, Sentinel-2, and Landsat-9 imagery to identify population and  infrastructure exposed to floods

Effective oil spill segmentation in Synthetic Aperture Radar (SAR) images is
critical for marine oil pollution cleanup, and proper image representation is
helpful for accurate image segmentation. In this paper, we propose an effective
oil spill image segmentation network named SRCNet by leveraging SAR image
representation and the training for oil spill segmentation simultaneously.
Specifically, our proposed segmentation network is constructed with a pair of
deep neural nets with the collaboration of the seminal representation that
describes SAR images, where one deep neural net is the generative net which
strives to produce oil spill segmentation maps, and the other is the
discriminative net which trys its best to distinguish between the produced and
the true segmentations, and they thus built a two-player game. Particularly,
the seminal representation exploited in our proposed SRCNet originates from SAR
imagery, modelling with the internal characteristics of SAR images. Thus, in
the training process, the collaborated seminal representation empowers the
mapped generative net to produce accurate oil spill segmentation maps
efficiently with small amount of training data, promoting the discriminative
net reaching its optimal solution at a fast speed. Therefore, our proposed
SRCNet operates effective oil spill segmentation in an economical and efficient
manner. Additionally, to increase the segmentation capability of the proposed
segmentation network in terms of accurately delineating oil spill details in
SAR images, a regularisation term that penalises the segmentation loss is
devised. This encourages our proposed SRCNet for accurately segmenting oil
spill areas from SAR images. Empirical experimental evaluations from different
metrics validate the effectiveness of our proposed SRCNet for oil spill image
segmentation.

该研究提出了一个名为 SRCNet 的有效油污图像分割网络，利用合成孔径雷达（SAR）图像表示和油污分割培训同时进行，该网络由一对协作的深度神经网络构成，在训练过程中，利用 SAR 图像的内在特性，与正解油污分割图的相关特征进行协同表示。结果表明，SRCNet 能够在经济高效的情况下对油污图像进行有效的分割。