Active Learning: Any Value for Classification of Remotely Sensed Data?

Melba M. Crawford, Devis Tuia, Hsiuhan Lexie Yang
2013 Proceedings of the IEEE  
Active learning, which has a strong impact on processing data prior to the classification phase, is an active research area within the machine learning community, and is now being extended for remote sensing applications. To be effective, classification must rely on the most informative pixels, while the training set should be as compact as possible. Active learning heuristics provide capability to select unlabeled data that are the "most informative" and to obtain the respective labels,
more » ... uting to both goals. Characteristics of remotely sensed image data provide both challenges and opportunities to exploit the potential advantages of active learning. We provide an overview of active learning methods, then review the latest techniques proposed to cope with the problem of interactive sampling of training pixels for classification of remotely sensed data with support vector machines. We discuss remote sensing specific approaches dealing with multi-source and spatially and time-varying data, and provide examples for high dimensional hyperspectral and very high resolution multispectral imagery.
doi:10.1109/jproc.2012.2231951 fatcat:hoyz7vgagfgqfoxjfmwqdjqkvu