A hyperspectral band selection method based on sparse band attention network for maize seed variety identification
文献类型： 外文期刊
作者： Zhang, Liu 1 ; Wei, Yaoguang 1 ; Liu, Jincun 1 ; Wu, Jianwei 5 ; An, Dong 1 ;
作者机构： 1.China Agr Univ, Natl Innovat Ctr Digital Fishery, Beijing 100083, Peoples R China
2.China Agr Univ, Minist Agr & Rural Affairs, Key Lab Smart Farming Technol Aquat Anim & Livesto, Beijing 100083, Peoples R China
3.Beijing Engn & Technol Res Ctr Internet Things Agr, Beijing 100083, Peoples R China
4.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China
5.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100097, Peoples R China
6.Beijing PAIDE Sci & Technol Dev Co Ltd, Beijing 100097, Peoples R China
关键词： Hyperspectral imaging; Band selection; Attention mechanism; Deep learning; Seed variety identification
期刊名称：EXPERT SYSTEMS WITH APPLICATIONS （ 影响因子：8.5； 五年影响因子：8.3 ）
ISSN： 0957-4174
年卷期： 2024 年 238 卷
页码：
收录情况： SCI
摘要： The development of a real-time online system for rapid and nondestructive identification of seed varieties can greatly improve production efficiency in modern agriculture. Hyperspectral imaging (HSI) is a powerful tool for seed variety identification. Nevertheless, hyperspectral data are not only high in dimensionality but also contain redundant information, which is very unfriendly to real-time online applications. Selecting a few representative bands from the entire working spectral region can significantly reduce the equipment cost and computational load of HSI. In the field of food and agr-products quality evaluation, Band selection (BS) methods based on chemometrics have been dominant for a long time. Most of these methods, however, fail to take full account of the nonlinearities and global interactions between spectral bands, which may result in the selection of some adjacent bands that still retain more redundant information. In this paper, a novel BS network is proposed, which is composed of sparse band attention module and classification net module. The former is used to generate weight of each band, and sparse constraint is applied to the weights of redundant bands, while the latter is used to achieve high-performance classification with reweighted data. Furthermore, to solve the problem of gradient updating caused by sparse constraint, a auxiliary loss function is defined to assist optimization. Finally, comparative experiments is conducted on our maize seed hyperspectral dataset. The results demonstrate that the presented method selects a subset of informative bands with less redundant information to obtain better clas-sification performance and outperforms several other existing BS methods.
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