UniHSFormer X for Hyperspectral Crop Classification with Prototype-Routed Semantic Structuring

Zhen Du, Senhao Liu, Yao Liao, Yuanyuan Tang, Yanwen Liu, Huimin Xing, Zhijie Zhang and Donghui Zhang ()
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Zhen Du: School of Economics and Management, East China University of Technology, Nanchang 330013, China
Senhao Liu: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Yao Liao: Guizhou Ecological Meteorology and Agrometeorology Center, Guiyang 550002, China
Yuanyuan Tang: Changsha Natural Resources Comprehensive Survey Center, China Geological Survey, Changsha 410000, China
Yanwen Liu: School of Resources and Environment Science and Engineering, Hubei University of Science and Technology, Xianning 437100, China
Huimin Xing: College of Surveying and Planning, Shangqiu Normal University, Shangqiu 476000, China
Zhijie Zhang: School of Geography, Development and Environment, The University of Arizona, Tucson, AZ 85719, USA
Donghui Zhang: Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China

Agriculture, 2025, vol. 15, issue 13, 1-32

Abstract: Hyperspectral imaging (HSI) plays a pivotal role in modern agriculture by capturing fine-grained spectral signatures that support crop classification, health assessment, and land-use monitoring. However, the transition from raw spectral data to reliable semantic understanding remains challenging—particularly under fragmented planting patterns, spectral ambiguity, and spatial heterogeneity. To address these limitations, we propose UniHSFormer-X, a unified transformer-based framework that reconstructs agricultural semantics through prototype-guided token routing and hierarchical context modeling. Unlike conventional models that treat spectral–spatial features uniformly, UniHSFormer-X dynamically modulates information flow based on class-aware affinities, enabling precise delineation of field boundaries and robust recognition of spectrally entangled crop types. Evaluated on three UAV-based benchmarks—WHU-Hi-LongKou, HanChuan, and HongHu—the model achieves up to 99.80% overall accuracy and 99.28% average accuracy, outperforming state-of-the-art CNN, ViT, and hybrid architectures across both structured and heterogeneous agricultural scenarios. Ablation studies further reveal the critical role of semantic routing and prototype projection in stabilizing model behavior, while parameter surface analysis demonstrates consistent generalization across diverse configurations. Beyond high performance, UniHSFormer-X offers a semantically interpretable architecture that adapts to the spatial logic and compositional nuance of agricultural imagery, representing a forward step toward robust and scalable crop classification.

Keywords: hyperspectral imaging; crop classification; agricultural remote sensing; transformer architecture; semantic routing; prototype projection; semantic segmentation; UAV-based monitoring (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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