3D-TabNetHS: A hyperspectral image classification method based on improved interpretable 3D attentive TabNet

Abstract

The classification methods for hyperspectral images (HSI) based on decision trees and convolutional neural networks have shown increasing advantages, but these methods often require a large number of labelled samples for learning, which is difficult for HSI, and the interpretability of the network is not high. Therefore, this paper proposes classification methods based on improved attention interpretable table learning (TabNet) named 3D TabNet HSI (3D-TabNetHS) and unsupervised 3D TabNet HSI (U3D-TabNetHS). These methods use sequential attention to select appropriate HSI spatial-spectral features and add a space spectral information extraction (SSE) module composed of a 3D convolutional neural network (3D-CNN) and fully connected layers to the Attention Transformer module in the original TabNet network to extract spatial-spectral soft features. At the same time, unsupervised learning can be used to retrain the 3D-TabNetHS network, and the classification accuracy of the resulting U3D-TabNetHS network can be further improved. Compared with other HSI classification methods based on decision trees, the HSI classification accuracy of 3D-TabNetHS is higher. On three typical HSI datasets, the accuracy metric overall accuracy of 3D-TabNetHS reached as high as 98.71%, 94.73%, and 97.23%, respectively. Simultaneously, the consistency evaluation metric Kappa also reached 98.56%, 93.98%, and 96.31% respectively. The experimental results indicate the feasibility and reliability of the proposed method in HSI classification.