Cross-Dataset Hyperspectral Image Classification Based on Adversarial Domain Adaptation

The cross-data set knowledge is vital for hyperspectral image classification, which can reduce the dependence on the sample quantity by transferring knowledge from other data sets and improve the training efficiency by sharing knowledge between different data sets. However, due to the capturing environment change and imaging equipment difference, domain shift troubles the exploitation of the cross-data set knowledge. To address the aforementioned issue, this article proposes an unsupervised cross-data set hyperspectral image classification method based on adversarial domain adaptation. The proposed method, which employs multiple classifiers to build a discriminator and uses variational autoencoders to constitute a generator, works in an adversarial manner to drive the target samples under the support of the source domain. In particular, the classification error and the classification disagreement are considered in the objective function, which helps to align different domains while keeping the boundaries of different classes. Experimental results of the multidomain data set demonstrate that the proposed method can transfer and share cross-data set knowledge and achieve state-of-the-art performance without using the labeled information of the target data set.

Automated summary

This article proposes an unsupervised cross-data set hyperspectral image classification method based on adversarial domain adaptation, which effectively reduces domain shift issues through the use of multiple classifiers and variational autoencoders. By considering classification error and disagreement, the method aligns different domains while maintaining class boundaries, achieving state-of-the-art performance in transferring and sharing knowledge across data sets without using labeled information of the target data set.