Deep Composite Kernels ELM Based on Spatial Feature Extraction for Hyperspectral Vegetation Image Classification

Abstract

Vegetation classification has a pivotal role in forest management and ecological research. It is a specific application problem in hyperspectral image classification. However, the existing classification models do not make sufficient use of the spatial features of vegetation, and cannot extract deep feature information. To address these issues, we propose a deep composite kernel extreme learning machine based on spatial feature extraction (DCKELM-SPATIAL) to classify vegetation. Especially, we use the Gabor filter and super-pixel density peak clustering method to obtain a new set of spatial composite kernels. Experiments are carried out on two sets of real hyperspectral vegetation datasets. The results show that this method is superior to some classical and advanced methods in classification accuracy, and satisfactory results are obtained.