PixelDINO：用于检测永久冻土扰动的半监督语义分割
PixelDINO: Semi-Supervised Semantic Segmentation for Detecting Permafrost Disturbances
AI总结：
本文提出半监督语义分割框架PixelDINO，通过在标注与未标注数据上并行训练并利用伪类别一致性约束，提升北极永久冻土扰动RTS的遥感检测性能与跨区域泛化能力。
摘要：
由于全球气候变化，北极永久冻土正面临显著变化。由于这些地区在很大程度上难以进入，遥感在更好地理解其潜在过程方面发挥着至关重要的作用，不仅在局部尺度上，而且在整个北极地区。在本研究中，我们重点关注逆行融滑塌（RTS）的遥感检测，这是一种由融化引发的、可与滑坡相比的永久冻土扰动。对于此类基于太空的分析，深度学习已成为不可或缺的工具，但有限的标注训练数据仍然是训练精确模型的挑战。为了在不需要额外标注数据的情况下提高模型在整个北极地区的泛化能力，我们提出了一种半监督学习方法，用于训练语义分割模型以检测RTS。我们名为PixelDINO的框架在标注数据和未标注数据上并行训练。对于未标注数据，模型将影像分割为自学习的伪类别，并且训练过程确保这些伪类别在输入数据的强增强下保持一致。我们的实验结果表明，PixelDINO既能优于有监督基线方法，也能优于现有的半监督语义分割方法，凸显了其在训练鲁棒模型方面的潜力，这些模型能够很好地泛化到未包含在训练数据中的区域。包含代码和本研究其他材料的项目页面可在\url{https://khdlr.github.io/PixelDINO/}找到。
英文摘要：
Arctic Permafrost is facing significant changes due to global climate change. As these regions are largely inaccessible, remote sensing plays a crucial rule in better understanding the underlying processes not just on a local scale, but across the Arctic. In this study, we focus on the remote detection of retrogressive thaw slumps (RTS), a permafrost disturbance comparable to landslides induced by thawing. For such analyses from space, deep learning has become an indispensable tool, but limited labelled training data remains a challenge for training accurate models. To improve model generalization across the Arctic without the need for additional labelled data, we present a semi-supervised learning approach to train semantic segmentation models to detect RTS. Our framework called PixelDINO is trained in parallel on labelled data as well as unlabelled data. For the unlabelled data, the model segments the imagery into self-taught pseudo-classes and the training procedure ensures consistency of these pseudo-classes across strong augmentations of the input data. Our experimental results demonstrate that PixelDINO can improve model performance both over supervised baseline methods as well as existing semi-supervised semantic segmentation approaches, highlighting its potential for training robust models that generalize well to regions that were not included in the training data. The project page containing code and other materials for this study can be found at \url{https://khdlr.github.io/PixelDINO/}.
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。
