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Task-driven and interpretable hyperspectral band selection via deep sequential modeling: A case study on apple bruise detection

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成果类型:
期刊论文
作者:
Shiwei Chen;Chaoxian Liu;Shan Zeng*;Chengyu Zhang;Weiqiang Yang;...
通讯作者:
Shan Zeng
作者机构:
[Shiwei Chen; Chaoxian Liu; Shan Zeng; Wei Tao; Zhiguang Yang] Wuhan Polytechnic University, Wuhan, 430023, China
[Chengyu Zhang] Southwest Jiaotong University, Chengdu, 611756, China
[Weiqiang Yang] Northwest A&F University, Yangling, 712100, China
通讯机构:
[Shan Zeng] W
Wuhan Polytechnic University, Wuhan, 430023, China
语种:
英文
期刊:
Food Control
ISSN:
0956-7135
年:
2026
卷:
181
页码:
111740
基金类别:
CRediT authorship contribution statement Shiwei Chen: Writing – review & editing, Writing – original draft, Visualization, Methodology, Data curation, Conceptualization. Chaoxian Liu: Writing – review & editing, Writing – original draft, Methodology, acquisition, Conceptualization. Shan Zeng: Writing – review & editing, Validation, Supervision, Software, Project administration, Methodology, acquisition, Conceptualization. Chengyu Zhang: Validation, Supervision, Investigation, Conceptualization. Weiqiang Yang:
机构署名:
本校为第一且通讯机构
摘要:
Hyperspectral imaging (HSI) has emerged as a powerful non-destructive sensing technology for detailed quality assessment of agricultural and food products. However, the high dimensionality, redundancy, and nonlinear inter-band dependencies inherent in HSI data present major challenges for model efficiency, robustness, and interpretability. Traditional band selection methods often rely on linear assumptions, neglect inter-band dependencies, fail to model hierarchical spectral features, and lack task-specific adaptability, thereby limiting their practical utility. To overcome these limitations, ...

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