Machine learning algorithms have demonstrated impressive results for land cover mapping from hyperspectral data. To enhance generalization capabilities of statistical models, active learning (AL) methods guide the annotation of the training data set by querying the most informative samples. The training of the classifier can then be performed on an optimal training data set. We bring under the same framework uncertainty, representativeness, and performance-based AL techniques; conduct a benchmark on state-of-the-art methods and release a toolbox ( https://github.com/Romain3Ch216/AL4EO ) to allow experimentation with these approaches. The experiments are conducted on various data sets: a toy data set, classic hyperspectral benchmark data sets, and a complex hyperspectral scene. We evaluate the methods with usual accuracy metrics as well as complementary metrics, which allow us to provide guidelines when choosing a relevant AL strategy in a real use case.

中文翻译：

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高光谱图像分类的主动学习：比较综述


机器学习算法已经在利用高光谱数据绘制土地覆盖图方面展示了令人印象深刻的结果。为了增强统计模型的泛化能力，主动学习（AL）方法通过查询信息最丰富的样本来指导训练数据集的注释。然后可以在最佳训练数据集上执行分类器的训练。我们将不确定性、代表性和基于性能的 AL 技术置于同一框架下；对最先进的方法进行基准测试并发布一个工具箱（https://github.com/Romain3Ch216/AL4EO）以允许对这些方法进行实验。实验在各种数据集上进行：玩具数据集、经典高光谱基准数据集和复杂的高光谱场景。我们使用常用的准确性指标和补充指标来评估这些方法，这使我们能够在实际用例中选择相关的 AL 策略时提供指导。