The AA of kernel size in the experiment.

In recent years, the advancement of hyperspectral remote sensing technology has greatly enhanced the detailed mapping of tree species. Nevertheless, delving deep into the significance of hyperspectral remote sensing data features for tree species recognition remains a challenging endeavor. The method of Hybrid-CS was proposed to addresses this challenge by synergizing the strengths of both deep learning and traditional learning techniques. Initially, we extract comprehensive correlation structures and spectral features. Subsequently, a hybrid approach, combining correlation-based feature selection with an optimized recursive feature elimination algorithm, identifies the most valuable feature set. We leverage the Support Vector Machine algorithm to evaluate feature importance and perform classification. Through rigorous experimentation, we evaluate the robustness of hyperspectral image-derived features and compare our method with other state-of-the-art classification methods. The results demonstrate: (1) Superior classification accuracy compared to traditional machine learning methods (e.g., SVM, RF) and advanced deep learning approaches on the tree species dataset. (2) Enhanced classification accuracy achieved by incorporating SVM and CNN information, particularly with the integration of attention mechanisms into the network architecture. Additionally, the classification performance of a two-branch network surpasses that of a single-branch network. (3) Consistent high accuracy across different proportions of training samples, indicating the stability and robustness of the method. This study underscores the potential of hyperspectral images and our proposed methodology for achieving precise tree species classification, thus holding significant promise for applications in forest resource management and monitoring.

近年来，高光谱遥感（hyperspectral remote sensing）技术的进步极大推动了树种精细制图工作的开展。然而，深入探究高光谱遥感数据特征在树种识别中的价值仍是一项颇具挑战的工作。本研究提出Hybrid-CS方法，通过协同整合深度学习与传统学习技术的优势来解决上述挑战。首先，提取全面的关联结构与光谱特征；随后，采用结合基于关联特征选择与优化递归特征消除算法的混合方法，筛选出最具价值的特征子集。本研究借助支持向量机（Support Vector Machine, SVM）算法评估特征重要性并完成分类任务。通过严谨的实验，本研究评估了高光谱图像衍生特征的鲁棒性，并将所提方法与其他前沿分类方法进行对比。研究结果显示：（1）在树种数据集上，本方法的分类精度优于传统机器学习方法（如SVM、随机森林RF）以及先进深度学习方法；（2）融合SVM与卷积神经网络（Convolutional Neural Network, CNN）信息可提升分类精度，尤其在网络架构中集成注意力机制后，分类精度进一步提升；此外，双分支网络的分类性能优于单分支网络；（3）在不同训练样本占比下均能保持稳定的高精度，表明本方法具备良好的稳定性与鲁棒性。本研究证实了高光谱图像与所提方法在精准树种分类中的应用潜力，因此在森林资源管理与监测领域具备广阔的应用前景。