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arXiv · 2208.08675

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

Debmita Bandyopadhyay·Subhadip Mukherjee·James Ball·Grégoire Vincent·David A. Coomes·Carola-Bibiane Schönlieb
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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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Debmita Bandyopadhyay, Subhadip Mukherjee, James Ball, Grégoire Vincent, David A. Coomes, Carola-Bibiane Schönlieb. 2022-08-18. Tree species classification from hyperspectral data using graph-regularized neural networks. https://arxiv.org/abs/2208.08675

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