Sandwich Convolutional Neural Network for Hyperspectral Image Classification Using Spectral Feature Enhancement
Hongmin Gao, Zhonghao Chen, Chenming Li
Abstract
Recently, convolutional neural networks (CNNs) have been used to extract spectral and spatial features of hyperspectral images (HSIs) for hyperspectral image classification (HSIC) because of their excellent performance in extracting and analyzing complex data. However, due to the limited labeled samples and existing mixed pixels, it is difficult to extract features effectively, which further leads to the problem of overfitting of the model. On the other hand, to improve the extraction ability of the CNN, the depth of the model, and the complexity of the convolution kernel often need to be increased. In this article, a sandwich CNN based on spectral feature enhancement (SFE-SCNN) is proposed for HSIC. The proposed method, SFE-SCNN, introduces the spectral feature enhancement operation, which makes the data reflect more discriminative spectral feature details to suppress the interference of mixed pixels. Furthermore, according to the preprocessed data structure features, a lightweight sandwich convolution neural network is proposed. To fully extract the spectral features, the spectral feature re-extraction operation is used for the first time. Experimental results on three real hyperspectral datasets demonstrate that the proposed method achieves better classification performance than other state-of-the-art methods.