In recent years, the application of deep learning (DL) has revolutionized remote sensing (RS) image analysis, allowing for the extraction of high-level features, and addressing complex tasks. However, the success of DL models relies heavily on the availability of labeled data, and acquiring labeled samples in the RS domain can be particularly challenging due to factors such as cost, time, and the dynamic nature of landscapes. Active learning (AL) has been a well-established concept in RS imagery analysis, even predating the widespread adoption of DL. Its significance lies in its ability to iteratively select the most informative samples from the unlabeled dataset, reducing the annotation cost and improving model performance with limited labeled data. In the era of DL, where the demand for labeled samples is higher than ever, AL has become increasingly crucial. Deep AL is an innovative and intricate approach that seeks to harness the strengths of both DL and AL methodologies. This integration aims to improve the performance of DL models while reducing the reliance on large amounts of labeled data. Integrating AL with deep architectures presents challenges but offers a promising approach to RS tasks.

中文翻译：

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遥感图像分析深度主动学习的回顾与展望：增强对用户猜想的适应


近年来，深度学习 (DL) 的应用彻底改变了遥感 (RS) 图像分析，允许提取高级特征并解决复杂的任务。然而，深度学习模型的成功在很大程度上依赖于标记数据的可用性，并且由于成本、时间和景观的动态性质等因素，在 RS 域中获取标记样本可能特别具有挑战性。主动学习 (AL) 已经成为遥感图像分析中一个成熟的概念，甚至早于深度学习的广泛采用。其意义在于能够从未标记的数据集中迭代地选择信息量最大的样本，从而降低标注成本并利用有限的标记数据提高模型性能。在深度学习时代，对标记样本的需求比以往任何时候都更高，AL 变得越来越重要。深度 AL 是一种创新且复杂的方法，旨在利用 DL 和 AL 方法的优势。这种集成旨在提高深度学习模型的性能，同时减少对大量标记数据的依赖。将 AL 与深度架构集成带来了挑战，但为 RS 任务提供了一种有前途的方法。