论文标题
内核极限学习机由Sparrow搜索算法优化高光谱图像分类
Kernel Extreme Learning Machine Optimized by the Sparrow Search Algorithm for Hyperspectral Image Classification
论文作者
论文摘要
为了提高高光谱图像分类算法的分类性能和概括能力，本文使用多尺度的总变异（MSTV）提取光谱特征，局部二进制图案（LBP）来提取空间特征，并提取特征叠加来获得高光谱图像的融合特征。具有高收敛性和强大的全局搜索能力的新的群智能优化方法，Sparrow Search算法（SSA）用于优化内核极端学习机（KELM）的内核参数和正规化系数。总而言之，本文提出了多尺度融合特征高光谱图像分类方法（MLS-KELM）。选择了印度松树，帕维亚大学和休斯顿2013年数据集来验证MLS-KELM的分类性能，并将该方法应用于ZY1-02D高光谱数据。实验结果表明，与其他流行的分类方法相比，MLS-KELM具有更好的分类性能和概括能力，而MLS-KELM在小样本案例中显示出其强大的鲁棒性。
To improve the classification performance and generalization ability of the hyperspectral image classification algorithm, this paper uses Multi-Scale Total Variation (MSTV) to extract the spectral features, local binary pattern (LBP) to extract spatial features, and feature superposition to obtain the fused features of hyperspectral images. A new swarm intelligence optimization method with high convergence and strong global search capability, the Sparrow Search Algorithm (SSA), is used to optimize the kernel parameters and regularization coefficients of the Kernel Extreme Learning Machine (KELM). In summary, a multiscale fusion feature hyperspectral image classification method (MLS-KELM) is proposed in this paper. The Indian Pines, Pavia University and Houston 2013 datasets were selected to validate the classification performance of MLS-KELM, and the method was applied to ZY1-02D hyperspectral data. The experimental results show that MLS-KELM has better classification performance and generalization ability compared with other popular classification methods, and MLS-KELM shows its strong robustness in the small sample case.
