2009/04/08 by Devis Tuia, D. Tuia, Frédéric Ratle +7 · 4 citations
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Machine Learning and Algorithms #Remote-Sensing Image Classification
paper · doi:10.1109/tgrs.2008.2010404
openalex publication_date 2009/04/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.