There are two heterogeneous data types in hyperspectral image (HSI): rich spectral data and spatial information. Recent research has shown that the application of spectral–spatial information significantly improves HSI accuracy; thus, multiple-feature combination-based methods have been favored by researchers in the field of HSI classification. A multiple-feature combination approach, based on the particle swarm optimization (PSO) algorithm, is proposed for improving the accuracy of HSI classification. The proposed method couples a multiple kernel support vector machine (SVM) with a PSO algorithm to assign optimal weights to different kernels. Moreover, it also solves the problem of artificially selecting weights when learning multiple features by implementing adaptive weights on different datasets. In addition, it has fewer parameters and a shorter training time than deep learning methods, thus, the model is smaller and easier to train. The proposed method was tested on four datasets, containing two and three kernels. The experimental results show that our optimized method improves the classification accuracy; additionally, the kappa performance of the classification is also better.

中文翻译：

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耦合粒子群优化和多核支持向量机的高光谱图像分类方法

高光谱图像（HSI）中有两种异构数据类型：丰富的光谱数据和空间信息。最近的研究表明，光谱空间信息的应用显着提高了 HSI 的准确性；因此，基于多特征组合的方法受到 HSI 分类领域研究人员的青睐。为了提高HSI分类的准确性，提出了一种基于粒子群优化（PSO）算法的多特征组合方法。所提出的方法将多核支持向量机 (SVM) 与 PSO 算法相结合，以将最佳权重分配给不同的核。此外，它还通过在不同数据集上实现自适应权重，解决了在学习多个特征时人为选择权重的问题。此外，它比深度学习方法具有更少的参数和更短的训练时间，因此模型更小，更容易训练。所提出的方法在四个数据集上进行了测试，包含两个和三个内核。实验结果表明，我们优化的方法提高了分类精度；此外，分类的 kappa 性能也更好。