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Active Versus Semi-supervised Learning Paradigm for the Classification of Remote Sensing Images

机译：主动对半监督学习范式的遥感影像分类

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摘要

This paper presents a comparative study in order to analyze active learning (AL) and semi-supervised learning (SSL) for the classification of remote sensing (RS) images. The two learning paradigms are analyzed both from the theoretical and experimental point of view. The aim of this work is to identify the advantages and disadvantages of AL and SSL methods, and to point out the boundary conditions on the applicability of these methods with respect to both the number of available labeled samples and the reliability of classification results. In our experimental analysis, AL and SSL techniques have been applied to the classification of both synthetic and real RS data, defining different classification problems starting from different initial training sets and considering different distributions of the classes. This analysis allowed us to derive important conclusion about the use of these classification approaches and to obtain insight about which one of the two approaches is more appropriate according to the specific classification problem, the available initial training set and the available budget for the acquisition of new labeled samples.
机译：本文提出了一项比较研究，以分析主动学习（AL）和半监督学习（SSL）来对遥感（RS）图像进行分类。从理论和实验的角度分析了这两种学习范式。这项工作的目的是确定AL和SSL方法的优缺点，并针对可用标记样品的数量和分类结果的可靠性指出这些方法的适用性的边界条件。在我们的实验分析中，AL和SSL技术已应用于合成和实际RS数据的分类，从不同的初始训练集开始并考虑类的不同分布来定义不同的分类问题。通过这种分析，我们可以得出关于使用这些分类方法的重要结论，并根据特定的分类问题，可用的初始培训集和用于购买新产品的可用预算，了解这两种方法中哪一种更合适。标记样品。

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