Hyperspectral remote sensing image classification based on dense residual three-dimensional convolutional neural network

Abstract

The invention discloses a hyperspectral remote sensing image classification method based on a dense residual three-dimensional convolutional neural network. According to the method, original hyperspectral data are used as network input, three-dimensional spatial-spectral features of a hyperspectral remote sensing image are extracted through three-dimensional convolution, the hyperspectral image can be directly processed through three-dimensional convolution, preprocessing operations such as dimension reduction are not needed, and the spatial-spectral features of the hyperspectral image are extracted more sufficiently. The dense residual network is used to deepen the number of network layers and learn deeper spectral and spatial features, the residual network can effectively reduce the problem of gradient disappearance along with the increase of the network depth, and the structure can more effectively utilize the features and enhance the feature transfer between convolutional layers. The training time is shortened through an early stop method, classification prediction is carried out through a Soft-max classifier, and an initial classification result is obtained; and proposing a multi-label conditional random field optimization algorithm, and optimizing a classification result. The method improves the operation efficiency, and improves the classification accuracy of the remotesensing images.