Tree species classification from hyperspectral data using graph-regularized neural networks

Manual labeling of tree species remains a challenging task, especially in tropical regions, owing to inaccessibility and labor-intensive ground-based surveys. Hyperspectral images (HSIs), through their narrow and contiguous bands, can assist in distinguishing tree species based on their spectral properties. Therefore, automated classification algorithms on HSI images can help augment the limited labeled information and generate a real-time classification map for various tree species. Achieving high classification accuracy with a limited amount of labeled information in an image is one of the key challenges that researchers have started addressing in recent years. We propose a novel graph-regularized neural network (GRNN) algorithm that encompasses the superpixel-based segmentation for graph construction, a pixel-wise neural network classifier, and the label propagation technique to generate an accurate classification map. 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 forests of French Guiana (FG) even when less than 1% of the pixels are labeled. We show that GRNN is not only competitive with the state-of-the-art semi-supervised methods, but also exhibits lower variance in accuracy for different number of training samples and over different independent random sampling of the labeled pixels for training.