[1]JIANG Xiongjie,ZHANG Hui,LIU Lizhu,et al.Superpixel sparse attention-guided hyperspectral image segmentation network for traditional Chinese medicine[J].CAAI Transactions on Intelligent Systems,2026,21(1):272-283.[doi:10.11992/tis.202507028]
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Superpixel sparse attention-guided hyperspectral image segmentation network for traditional Chinese medicine

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 1 Page number: 272-283 Column: 人工智能院长论坛 Public date: 2026-03-05
Title:
Superpixel sparse attention-guided hyperspectral image segmentation network for traditional Chinese medicine
Author(s):
JIANG Xiongjie1;  ZHANG Hui2; 3;  LIU Lizhu3;  YIN Ating3;  WANG Yaonan3
1. School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410004, China;
2. School of Artificial Intelligence and Robotics, Hunan University, Changsha 410082, China;
3. National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University, Changsha 410082, China
Keywords:
traditional Chinese medicine quality identification; hyperspectral images; attention mechanism; semantic segmentation; feature extraction; cross-scale fusion; superpixel; sparse attention
CLC:
TP391.4；TH744
DOI:
10.11992/tis.202507028
Abstract:
In the context of quality control in traditional Chinese medicine, to address the limitations of conventional RGB-based methods—which struggle to achieve accurate identification due to the lack of spectral information—and the challenges faced by existing hyperspectral approaches in scenarios characterized by small inter-class differences and large intra-class variations, a line-scan hyperspectral imaging system was developed and five pixel-level annotated hyperspectral datasets were constructed for TCM materials. Moreover, a spatial–spectral superpixel, sparse attention-guided hyperspectral image segmentation network was proposed for non-destructive, high-precision quality detection via a three-stage architecture. First, the network segments homogeneous regions of the hyperspectral image into superpixel blocks based on the spatial and spectral distances to enhance the semantic consistency and improve feature learning. Thereafter, a dual-stream superpixel sparse attention module constructs a superpixel correlation matrix, filters out irrelevant regions, and enables the extraction of global spatial features and the modeling of spectral band saliency in strongly correlated areas. Finally, a multi-scale feature fusion decoder achieves pixel-level segmentation. As demonstrated experimentally, the proposed method achieves an mean pixel accuracy(MPA) of 0.968 and an mean intersection over union(MIoU) of 0.933 across datasets for authenticity identification, origin tracing, and processing-type classification, outperforming existing approaches. These results indicate that the proposed framework offers a robust and effective solution for precise quality assessment of traditional Chinese medicinal materials.
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