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.

翻译：我们提出一种新颖的图正则化神经网络算法用于树种分类。该算法包含基于超像素分割的图构建、逐像素神经网络分类器以及标签传播技术，能够在稀疏标注数据集上生成精确且符合树冠实际形态的分类图。与多种前沿技术相比，GRNN不仅在标准Indian Pines高光谱影像上表现优异，还在法属圭亚那异质性森林采集的新高光谱数据集上（标注像素不足1%）实现了约92%的高分类精度。我们进一步证明GRNN与当前最先进的半监督方法具有竞争力，且在训练样本数量变化以及重复随机采样标注像素训练时，分类精度波动较小。