Active learning based autoencoder for hyperspectral imagery classification
In this paper, we joint autoencoder with active learning for hyperspectral imagery classification. Specifically, we learn the classifier via autoencoder, where the most informative samples are acitvely selected through the interaction between the autoencoder and active learning. Experimental results, conducted using both the Kennedy Space Center and the Indian Pines hyperspectral images, show that driven by active learning, the performance of autoencoder can be greatly improved.