A Hyperspectral Image Classification Method Using Multifeature Vectors and Optimized KELM

Abstract

To improve the accuracy and generalization ability of hyperspectral image classification, a feature extraction method integrating principal component analysis (PCA) and local binary pattern (LBP) is developed for hyperspectral images in this article. The PCA is employed to reduce the dimension of the spectral features of hyperspectral images. The LBP with low computational complexity is used to extract the local spatial texture features of hyperspectral images to construct multifeature vectors. Then, the gray wolf optimization algorithm with global search capability is employed to optimize the parameters of kernel extreme learning machine (KELM) to construct an optimized KELM model, which is used to effectively realize a hyperspectral image classification (PLG-KELM) method. Finally, the Indian pines dataset, Houston dataset, and Pavia University dataset and an application of WHU-Hi-LongKou dataset are selected to verify the effectiveness of the PLG-KELM. The comparison experiment results show that the PLG-KELM can obtain higher classification accuracy, and takes on better generalization ability for small samples. It provides a new idea for processing hyperspectral images.

Keywords

• Gray wolf optimization (GWO)
• hyperspectral image classification
• kernel extreme learning machine (KELM)
• local binary pattern (LBP)
• optimization
• principal component analysis (PCA)