Hyperspectral images (HSI) are widely applied in numerous fields for their rich spatial and spectral information. However, in these applications, we always face the situation that the available labeled samples are limited or absent. Therefore, we propose a HSI classification method based on domain adaptation broad learning (DABL). Firstly, according to the importance of the marginal and conditional distributions, the maximum mean discrepancy is used in mapped features to adapt these distributions between source and target domains. Meanwhile the manifold regularization is added to maintain the manifold structure of the input HSI data. Secondly, to further reduce the distribution difference and maintain manifold structure, the domain adaptation and manifold regularization are added to the output layer of DABL. Finally, the output weights can be easily calculated by the ridge regression theory. Experimental results on three real HSI datasets demonstrate the effectiveness of our proposed DABL.
Hyperspectral image (HSI) classification has attracted much attention in the field of remote sensing. However, the lack of sufficient labeled training samples is a huge challenge for HSI classification. To face this challenge, we propose a semi-supervised HSI classification method based on graph convolutional broad network (GCBN). Firstly, to avoid the underfitting problem caused by the insufficient linear sparse feature representation ability of broad learning system (BLS), graph convolution operation is applied to extract non-linear and discriminative spectral-spatial features from the original HSI to replace the linear mapping features in the traditional BLS. Secondly, to solve the problem of insufficient model classification ability caused by limited labeled samples, the combinatorial average method (CAM) is proposed to use valuable paired samples to generate sample expansion set for GCBN model training. Thirdly, BLS is used to perform broad expansion on spectral-spatial features extracted by GCN and extended by CAM, which further enhances the feature representation ability. Finally, the output weights can be easily calculated by the ridge regression theory. Experimental results on three real HSI datasets demonstrate the effectiveness of our proposed GCBN.
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