Alternately Updated Spectral–Spatial Convolution Network for the Classification of Hyperspectral Images

Wenju Wang,Shuguang Dou,Sen Wang

doi:10.3390/rs11151794

Wenju Wang, Shuguang Dou + Show 1 more

Open Access

https://doi.org/10.3390/rs11151794

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Abstract

The connection structure in the convolutional layers of most deep learning-based algorithms used for the classification of hyperspectral images (HSIs) has typically been in the forward direction. In this study, an end-to-end alternately updated spectral–spatial convolutional network (AUSSC) with a recurrent feedback structure is used to learn refined spectral and spatial features for HSI classification. The proposed AUSSC includes alternating updated blocks in which each layer serves as both an input and an output for the other layers. The AUSSC can refine spectral and spatial features many times under fixed parameters. A center loss function is introduced as an auxiliary objective function to improve the discrimination of features acquired by the model. Additionally, the AUSSC utilizes smaller convolutional kernels than other convolutional neural network (CNN)-based methods to reduce the number of parameters and alleviate overfitting. The proposed method was implemented on four HSI data sets, as follows: Indian Pines, Kennedy Space Center, Salinas Scene, and Houston. Experimental results demonstrated that the proposed AUSSC outperformed the HSI classification accuracy obtained by state-of-the-art deep learning-based methods with a small number of training samples.

Highlights

Hyperspectral images (HSIs) contain both spectral and spatial information and generally consist of hundreds of spectral bands for the same observed scene [1]
An alternately updated spectral–spatial convolutional network is proposed for HSI classification
In this study, refined spectral and spatial features in HSIs were used as core concepts to design an end-to-end convolutional neural network (CNN)-based framework for HSI classification

Summary

Introduction

Hyperspectral images (HSIs) contain both spectral and spatial information and generally consist of hundreds of spectral bands for the same observed scene [1]. Traditional classification methods involve feature engineering using a classifier. This process aims to extract or select features from original HSI data, typically producing a classifier based on low-dimensional features. Support vector machines (SVMs) are the most commonly used method in the early stages of HSI classification, due to their low sensitivity to high dimensionality [6]. Mathematical-morphology-based techniques [8], Markov random fields (MRFs) [9], and sparse representations [10] are commonly used branches. Many of these techniques suffer from low classification accuracy due to shallow feature extraction

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