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

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 reali...

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Bibliographic Details
Published inarXiv.org
Main Authors Bandyopadhyay, Debmita, Mukherjee, Subhadip, Ball, James, Grégoire, Vincent, Coomes, David A, Schönlieb, Carola-Bibiane
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 05.05.2023
Subjects
Accuracy
Algorithms
Classification
Hyperspectral imaging
Image classification
Image segmentation
Neural networks
Pixels
Random sampling
Species classification
Training
Tropical environments
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Summary: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.
ISSN:2331-8422