Spectral-Spatial Distribution Consistent Network Based on Meta-Learning for Cross-Domain Hyperspectral Image Classification

Cross-domain networks can solve the problem of insufficient labeled samples, especially for hyperspectral images (HSIs) where obtaining labeled samples is time-consuming and laborious. Most of the current methods rely on the spatial information to achieve domain alignment, without considering the rich spectral information of HSIs. Furthermore, the methods based on convolutional neural network (CNN) cannot get the spatial information of irregular image regions, resulting in poor classification results of object edges. Therefore, we design a spectral-spatial distribution consistent network (SSDC) based on meta-learning. Firstly, to improve the feature extraction ability of the cross-domain classification model, we introduce a feature pre-extraction module, which uses the spectral attention mechanism and the alternating meta-learning method to obtain the general features of the source domain and the discriminative features of the target domain, so as to obtain the spectral weight matrix for subsequent processing. Secondly, we propose a spectral consistent module based on singular value decomposition, which increases the difference between different classes of features by penalizing the singular values of the feature matrix to achieve data distribution alignment in the spectral dimension. Finally, aiming at the low classification accuracy of irregular image regions, we propose a spatial consistent module to obtain non-local spatial topological information through stacked cross modules and graph sample and aggregate networks, which can reduce domain shift. The experiments of SSDC on four classical HSI datasets show that the proposed method can obtain competitive results with other methods based on CNN and cross-domain.