Unsupervised satellite image classification based on partial transfer learning

Satellite image classification plays an important role in many fields. It can be divided into two groups: supervised and unsupervised classification. The former requires a large number of labeled data, while, in practice, satellite images usually lack of sufficient ones. How to achieve high accuracy on unsupervised satellite image classification is a key problem. For tackling this problem, based on the idea of partial transfer learning, we propose an novel end-to-end unsupervised classification method called coordinate partial adversarial domain adaptation (CPADA) for satellite image classification. Under the aid of a novel coordinate loss, our framework transfers relevant examples in the shared classes to promote performance, and ignore irrelevant ones in the specific classes to mitigate negative transfer. Experiments show that our CPADA exceeds state-of-the-art results for unsupervised satellite image classification task.