Adapting Active Learning to Improve Hyperspectral Image Classification within Supervised Learning

Abstract

The performance of hyperspectral image classification (HIC) models strongly depends on the informativeness and representativeness of the training data, which directly impacts classification accuracy. Active learning (AL) has been introduced as a strategy to enhance classification performance by selecting informative and representative samples from unlabeled data and incorporating them into the training process. Although AL has shown promising results in various applications, it requires an oracle to label new data. In this work, we eliminate the need for an oracle and adapt the principles of AL to the supervised learning paradigm. We integrate key concepts from AL into supervised learning by iteratively updating a supervised classifier with subsets of labeled and (potentially) informative data extracted from a fully labeled dataset. Experiments conducted on real hyperspectral data demonstrate that our method outperforms conventional supervised learning when implemented with a standard neural network architecture.