Adversarial Discriminative Active Deep Learning for Domain Adaptation in Hyperspectral Images Classification

ABSTRACT

Active deep learning (ADL) presents an appropriate solution for hyperspectral images (HSIs) classification based on domain adaptation (DA) with limited labelled samples in the target domain. But some challenges still exist. First, traditional ADL methods only match the feature distributions between the source and target domains globally without considering the decision boundaries between classes, which makes the ambiguous features near land-cover class boundaries and reduces the classification accuracy. Second, in previous ADL settings, a trained classifier is first used to obtain the predictions for the unlabelled data and then a measure is applied to achieve the uncertainty of such a classifier prediction. This two-step approach does not consider unlabelled data in the classifier training, which ignores dealing with noisy and complex data in the target domain. To overcome these issues, we propose adversarial discriminative active deep learning (ADADL), which presents an adversarial model and incorporates two different land-cover classifiers as a discriminator to consider the class boundaries in aligning feature distributions. Furthermore, ADADL combines the entropy measure along with the cross-entropy loss during training to use the information on the unlabelled data of the target domain. The experimental results with two benchmark HSIs show that the proposed ADADL creates robust transferable features far from the original class boundaries and improves the classification accuracy significantly compared to the state-of-art ADL methods even in complex and noisy data.

Disclosure statement

No potential conflict of interest was reported by the authors.