Abstract
Hyperspectral image (HSI) classification is a hot topic in remote sensing community; many researchers have made a great deal of effort in this domain. Recently, deep learning-based manner paves a new way to better classification accuracy. However, the flow of information between layers and layers (e.g. max-pooling) in traditional deep architecture turns out to be ineffective. In this study, a novel spectral–spatial classification framework for HSI based on Capsule Network (CapsNet) and dynamic routing algorithm is introduced. The proposed architecture is composed of a hybrid of 1D and 2D convolutional layers and two capsule layers for better and effective mining and combining features. Consequently, experiments on two popular dataset indicate that CapsNet-based framework outperforms traditional CNN-based counterparts. Moreover, this study reveals great potential for CapsNet model in the field of HSI classification.
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