A cost-sensitive active learning technique for the definition of effective training sets for supervised classifiers

This paper presents a novel cost-sensitive active learning technique (CSAL) to define effective training sets for the classification of remote sensing images. Unlike the standard active learning methods, the proposed technique redefines AL by assuming that the labeling cost of samples when ground survey is used is not uniform and depends both on the samples accessibility and the traveling time to the considered locations. Accordingly, the proposed CSAL technique is based on the joint evaluation of three criteria for the selection of the most informative samples that have a low labeling cost: i) uncertainty, ii) diversity and iii) cost efficiency. The labeling cost of the samples is assessed by using ancillary data like the road map and the digital elevation model of the considered area. Experimental results show the effectiveness of the proposed CSAL method compared to the standard active learning methods that neglect the labeling cost.