基于聚类引导权重学习的高光谱苹果叶片病害图像波段筛选方法OA

Aiming to address the issues of high band redundancy,heavy computational burden,and the difficulty of existing band selection methods in simultaneously balancing screening efficiency,recognition performance,and band physical interpretability in hyperspectral diagnosis of apple leaf diseases,a hyperspectral apple leaf disease image band selection method was proposed based on clustering-guided weight learning.Although traditional feature engineering methods can preserve the physical meaning of original bands,they insufficiently exploited the structural correlations among bands and had limitations in screening efficiency and generalization capability.End-to-end deep learning methods,while improving classification performance,struggle to explicitly quantify the actual contribution of individual bands to disease diagnosis,making it difficult to select bands with physical interpretability.To address these issues,the hierarchical clustering was firstly employed to structurally group hyperspectral bands.Secondly,a dual-branch convolutional neural network was constructed to learn the reconstruction fidelity weight and classification discriminability weight of bands respectively,achieving multi-dimensional quantitative assessment of band importance.Finally,an intra-cluster adaptive selection strategy was designed to select representative core bands from each cluster.Experiments conducted on hyperspectral data of five types of apple leaf diseases showed that the proposed method can select nine core bands from 216 effective bands,with an average screening time of 15.2 s per sample,achieving a classification accuracy of 98.38％in apple leaf disease diagnosis.The selected bands covered disease-sensitive spectral intervals such as the chlorophyll absorption band,red-edge transition region,and near-infrared water response,demonstrating that the method can significantly compress spectral dimensionality while effectively retaining key spectral information relevant to disease identification,and can provide a reference for the construction of lightweight disease diagnosis models and the band design of dedicated multispectral sensors.