  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.

Computer Vision and Pattern Recognition;Machine Learning