Definition of Effective Training Sets for Supervised Classification of Remote Sensing Images by a Novel Cost-Sensitive Active Learning Method

This paper proposes a novel cost-sensitive active learning (CSAL) method to the definition of reliable training sets for the classification of remote sensing images with support vector machines. Unlike standard active learning (AL) methods, the proposed CSAL method redefines AL by assuming that the labeling cost of samples during ground survey is not identical, but depends on both the samples accessibility and the traveling time to the considered locations. The proposed CSAL method selects the most informative samples on the basis of three criteria: 1) uncertainty; 2) diversity; and 3) labeling cost. The labeling cost of the samples is modeled by a novel cost function that exploits ancillary data such as the road network map and the digital elevation model of the considered area. In the proposed method, the three criteria are applied in two consecutive steps. In the first step, the most uncertain samples are selected, whereas in the second step the uncertain samples that are diverse and have low labeling cost are chosen. In order to select the uncertain samples that optimize the diversity and cost criteria, we propose two different optimization algorithms. The first algorithm is defined on the basis of a sequential forward selection optimization strategy, whereas the second one relies on a genetic algorithm. Experimental results show the effectiveness of the proposed CSAL method compared to standard AL methods that neglect the labeling cost.