干旱区生态环境知识资源中心(Arid): QPWS Feature Selection and CAE Fusion of Visible/Near-Infrared Spectroscopy Data for the Identification of Salix psammophila Origin

Arid

DOI	10.3390/f15010006
	QPWS Feature Selection and CAE Fusion of Visible/Near-Infrared Spectroscopy Data for the Identification of Salix psammophila Origin
	Ma, Yicheng; Li, Ying; Peng, Xinkai; Chen, Congyu; Li, Hengkai; Wang, Xinping; Wang, Weilong; Lan, Xiaozhen; Wang, Jixuan; Pei, Zhiyong
通讯作者	Pei, ZY
来源期刊	FORESTS
EISSN	1999-4907
出版年	2024
卷号	15 期号:1
英文摘要	Salix psammophila, classified under the Salicaceae family, is a deciduous, densely branched, and erect shrub. As a leading pioneer tree species in windbreak and sand stabilization, it has played a crucial role in combating desertification in northwestern China. However, different genetic sources of Salix psammophila exhibit significant variations in their effectiveness for windbreak and sand stabilization. Therefore, it is essential to establish a rapid and reliable method for identifying different Salix psammophila varieties. Visible and near-infrared (Vis-NIR) spectroscopy is currently a reliable non-destructive solution for origin traceability. This study introduced a novel feature selection strategy, called qualitative percentile weighted sampling (QPWS), based on the principle of the long tail effect for Vis-NIR spectroscopy. The core idea of QPWS combines weighted sampling and percentage wavelength selection to identify key wavelengths. By employing a multi-threaded parallel execution of multiple QPWS instances, we aimed to search for the optimal feature bands to address the instability issues that can arise during the feature selection process. To address the problem of reduced prediction performance in one-dimensional convolutional neural network (1D-CNN) models after feature selection, we have introduced convolutional autoencoders (CAEs) to reduce the dimensions of wavelengths that are discarded during feature selection. Subsequently, these reduced dimensions are fused with the selected wavelengths, thereby enhancing the model's performance. With our completed model, we selected outstanding models for model fusion and established a decision system for Salix psammophila. It is worth noting that all 1D-CNN models in this study were developed using Bayesian optimization methods. In comparison with principal component analysis (PCA) and full spectrum methods, QPWS exhibits superior predictive performance in the field of machine learning. In the realm of deep learning, the fusion of data combining QPWS with CAE demonstrated even greater potential with an improvement of average accuracy of approximately 2.13% when compared to QPWS alone and a 228% increase in operational speed compared to a model with full spectra. These results indicated that the combination of CAE with QPWS can be an effective tool for identifying the origin of Salix psammophila.
英文关键词	CAE 1D-CNN PLS-DA Vis-NIR origin traceability
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001151922200001
WOS关键词	GEOGRAPHICAL ORIGIN ; NIR ; DISCRIMINATION ; REFLECTANCE ; PREDICTION ; REGRESSION
WOS类目	Forestry
WOS研究方向	Forestry
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403768
推荐引用方式 GB/T 7714	Ma, Yicheng,Li, Ying,Peng, Xinkai,et al. QPWS Feature Selection and CAE Fusion of Visible/Near-Infrared Spectroscopy Data for the Identification of Salix psammophila Origin[J],2024,15(1).
APA	Ma, Yicheng.,Li, Ying.,Peng, Xinkai.,Chen, Congyu.,Li, Hengkai.,...&Pei, Zhiyong.(2024).QPWS Feature Selection and CAE Fusion of Visible/Near-Infrared Spectroscopy Data for the Identification of Salix psammophila Origin.FORESTS,15(1).
MLA	Ma, Yicheng,et al."QPWS Feature Selection and CAE Fusion of Visible/Near-Infrared Spectroscopy Data for the Identification of Salix psammophila Origin".FORESTS 15.1(2024).