An end-to-end active learning framework for limited labelled hyperspectral image classification

Abstract

Nonlinear characteristics, spectral variability, and high dimensionality pose significant challenges to the classification of hyperspectral images. Therefore, classifiers need more training samples to reach satisfactory results, particularly when only a small number of labeled samples are available. Existing active learning (AL) methods often neglect dimension reduction (DR), relying on the full spectrum, which limits their effectiveness. To address these issues, we use an end-to-end active learning framework that combines advanced preprocessing techniques, a probabilistic spatial-aware collaborative representation (PSACR) classifier, and a novel clustering-based active learning sampling strategy (MMU+CLSW) in a unified framework. The preprocessing phase, consisting of DR and domain transform filter (DtF), plays a crucial role in addressing spectral variability and high dimensionality. DR reduces the high dimensionality, and DtF helps in extracting important features, which together contribute to reducing intra-class variance and increasing inter-class variance, improving class separability. The probabilistic spatial aware collaborative representation (PSACR) classifier enables high accuracies to be achieved using these features under a limited training set. Moreover, sample selection is a crucial aspect of active learning. A novel clustering-based diversity and uncertainty sampling method (MMU+CLSW) is proposed for this purpose, leveraging collaborative weights obtained from PSACR. MMU+CLSW selects diverse and uncertain examples by clustering collaborative weights, benefiting from the intrinsic information of the PSACR classifier. We evaluate our method on three hyperspectral datasets: Indian Pines, Pavia University, and Salinas. Experimental results demonstrate the superiority of our approach over existing supervised and active learning techniques by achieving high classification accuracy even with an extremely limited number of labeled examples. Codes are available at https://github.com/alicankrc2/An-End-To-End-Active-Learning-Framework