Densely connected convolutional extreme learning machine for hyperspectral image classification

Abstract

Extreme Learning Machine (ELM) has gained lots of research interests due to its universal approximation capability and fast learning speed. Although several prior works have focused on developing deep ELM, it is still an open problem to design effective deep ELM. Stacking random layers will result in overfitting and accumulation of random errors. To address this issue, this paper presents a simple yet effective deep ELM called Densely Connected Convolutional ELM (DC2ELM) for hyperspectral image spectral-spatial classification. First, we introduce dense connection into ELM to make full use of intermediate feature maps produced by randomized convolutional layers, which is beneficial to reduce the random error. Secondly, stacked ELM auto-encoders are employed to generate reduced representation, leading to a deeper architecture. The proposed approach consists of fewer trainable parameters than traditional convolutional neural networks and can easily be trained without any iterative parameters tuning, making it easier to implement and apply in practice. We compare the proposed approach with many prior arts over three real hyperspectral images, showing that the proposed approach can achieve superior performance using limited training data and with a reduced risk of overfitting the training data.