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

• URL: http://arxiv.org/abs/2208.08675v2
• Date: Fri, 5 May 2023 12:25:49 GMT
• Title: Tree species classification from hyperspectral data using graph-regularized neural networks
• Authors: Debmita Bandyopadhyay, Subhadip Mukherjee, James Ball, Gr\'egoire Vincent, David A. Coomes, Carola-Bibiane Sch\"onlieb
• Abstract summary: We propose a 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. GRNN achieves a high classification accuracy (approx. 92%) on a new HSI data set collected over the heterogeneous forests of French Guiana.
• Score: 11.049203564925634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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