Abstract

Capsule networks can be considered to be the next era of deep learning and have recently shown their advantages in supervised classification. Instead of using scalar values to represent features, the capsule networks use vectors to represent features, which enriches the feature presentation capability. This paper introduces a deep capsule network for hyperspectral image (HSI) classification to improve the performance of the conventional convolutional neural networks (CNNs). Furthermore, a modification of the capsule network named Conv-Capsule is proposed. Instead of using full connections, local connections and shared transform matrices, which are the core ideas of CNNs, are used in the Conv-Capsule network architecture. In Conv-Capsule, the number of trainable parameters is reduced compared to the original capsule, which potentially mitigates the overfitting issue when the number of available training samples is limited. Specifically, we propose two schemes: (1) A 1D deep capsule network is designed for spectral classification, as a combination of principal component analysis, CNN, and the Conv-Capsule network, and (2) a 3D deep capsule network is designed for spectral-spatial classification, as a combination of extended multi-attribute profiles, CNN, and the Conv-Capsule network. The proposed classifiers are tested on three widely-used hyperspectral data sets. The obtained results reveal that the proposed models provide competitive results compared to the state-of-the-art methods, including kernel support vector machines, CNNs, and recurrent neural network.

Highlights

  • The task of classification, when it relates to hyperspectral images (HSIs), generally refers to assigning a label to each pixel vector in the scene [1]

  • They were captured over Salinas Valley in California (Salinas), Kennedy Space Center (KSC) in Florida, and an urban site over the University of Houston campus and the neighboring area (Houston)

  • After removing the low signal to noise ratio (SNR) bands, the available data set was composed of 204 bands with 512 × 217 pixels

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Summary

Introduction

The task of classification, when it relates to hyperspectral images (HSIs), generally refers to assigning a label to each pixel vector in the scene [1]. Due to the availability of abundant spectral information in HSIs, lots of spectral classifiers have been proposed for HSI classification including k-nearest-neighbors, maximum likelihood, neural network, logistic regression, and support vector machines (SVMs) [1,15,16]. Compared with other deep learning methods, there are two unique factors in the architecture of the CNN, i.e., local connections and shared weights. By using specific architectures like local connections and shared weights, CNN tends to provide better generalization for a wide variety of applications

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