  We propose a novel graph-regularized neural network (GRNN) algorithm for tree
species classification. The proposed algorithm encompasses superpixel-based
segmentation for graph construction, a pixel-wise neural network classifier,
and the label propagation technique to generate an accurate and realistic
(emulating tree crowns) classification map on a sparsely annotated data set.
GRNN outperforms several state-of-the-art techniques not only for the standard
Indian Pines HSI but also achieves a high classification accuracy (approx. 92%)
on a new HSI data set collected over the heterogeneous forests of French Guiana
(FG) when less than 1% of the pixels are labeled. We further show that GRNN is
competitive with the state-of-the-art semi-supervised methods and exhibits a
small deviation in accuracy for different numbers of training samples and over
repeated trials with randomly sampled labeled pixels for training.
