Hyperspectral image classification based on discriminative locality preserving broad learning system

Abstract

Recently, broad learning system (BLS) has been widely used for its simple, fast and excellent generalization ability in hyperspectral image (HSI) classification. However, how to implement a broad learning system for fine-grained classification of hyperspectral images with a few-shot setting is still a challenging problem. In this paper, we proposed a new method based on the discriminative locality preserving broad learning system (DPBLS) for hyperspectral image classification by exploiting the manifold structure between neighbouring pixels of hyperspectral image. To make full use of the spectral and spatial information of hyperspectral images.we firstly leverage edge-preserving filters to fuse both spectral and spatial features of hyperspectral image samples. Secondly, we introduce discriminative information and local manifold structure of samples into the broad learning system to enhance the discriminative ability of output weights and improve its performance on hyperspectral image classification task. In order to verify the performance of the framework proposed in this paper, we conducted experiments on four hyperspectral image datasets. experiment results show that the method we proposed is well-performed on hyperspectral image classification tasks.