Abstract
In this letter, we propose an efficient multiclass active learning (AL) method for remote sensing image classification. We fuse the capabilities of an extreme learning machine (ELM) classifier and graph-based optimization methods to boost the classification accuracy while minimizing the user interaction. First, we use the ELM to generate an initial label estimation of the unlabeled image pixels. Then, we optimize a graph-based functional energy that integrates the ELM outputs as an initial estimation of the image structure. As for the ELM, the solution to this multiclass optimization problem leads to a system of linear equations. Due to the sparse Laplacian matrix built from the lattice graph defined on the image pixels, the optimization problem is solved in a linear time. In the experiments, we report and discuss the results of the proposed AL method on two very high resolution images acquired by IKONOS-2 and GoeEye-1, as well as the well-known Pavia University hyperspectral image.