Hybrid Deep CNN-Attention for Hyperspectral Image Classification

Abstract

Convolutional neural networks (CNN) in deep learning can make full use of the computing power of computers to efficiently extract the features of remote sensing images. This has achieved good results, especially in the classification of hyperspectral images. In order to fully extract spectral and spatial features from limited hyper    spectral samples and improve the accuracy of hyperspectral image classification, a hybrid deep CNN-Attention (HDC-Attention) model is proposed. Firstly, this paper uses kernel principal component analysis (KPCA) and minibatch K-means (MBK-means) to reduce the dimensionality of hyperspectral images. This effectively eliminates data redundancy and retains the main amount of information. The result is that the dimensionality-reduced data have the best discrimination. Then, this paper uses the HDC network to extract the spectral-spatial features from the dimensionality-reduced data. Finally, the spectral-spatial attention is used to redistribute the weights of spectral-spatial features. It enhances useful spatial-spectral features and suppresses useless features. The proposed model has been tested on three public datasets for many times. With a limited number of labeled samples, the OA, AA, and Kappa classification indicators of the three datasets all exceed 99%.

Keywords

• hyperspectral image classification; kernel principal component analysis (kpca); convolutional neural networks (cnn); spectral-spatial attention mechanism; deep learning