Topology and Semantic Information Fusion Classification Network Based on Hyperspectral Images of Chinese Herbs

Abstract

Most methods for classifying hyperspectral data only consider the local spatial relationship among samples, ignoring the important non-local topological relationship. However, the non-local topological relationship is better at representing the structure of hyperspectral data. This paper proposes a deep learning model called Topology and semantic information fusion classification network (TSFnet) that incorporates a topology structure and semantic information transmission network to accurately classify traditional Chinese medicine in hyperspectral images. TSFnet uses a convolutional neural network (CNN) to extract features and a graph convolution network (GCN) to capture potential topological relationships among different types of Chinese herbal medicines. The results show that TSFnet outperforms other state-of-the-art deep learning classification algorithms in two different scenarios of herbal medicine datasets. Additionally, the proposed TSFnet model is lightweight and can be easily deployed for mobile herbal medicine classification.

Keywords

Chinese herbs / hyperspectral image / deep learning / non-local topological relationships / convolutional neural network (CNN) / graph convolutional network (GCN) / lightweight