An Open Set Domain Adaptation Network Based on Adversarial Learning for Remote Sensing Image Scene Classification
Jun Zhang, Jiao Liu, Lukui Shi, Bin Pan, Xia Xu
Abstract
Remote sensing image scene classification refers to assigning specific semantic labels for remote sensing images. Due to the lack of labeled remote sensing images, domain adaptation is applied to remote sensing image scene classification. However, recent proposed methods mainly focus on the closed set scenario. In this paper, we explore the open set scenario and introduce an open set domain adaptation network (OSDANet) for remote sensing image scene classification. Inspired by the idea of Generative Adversarial Network (GAN), we design a feature generator as well as a classifier which are learnt in an adversarial way. The purpose of the classifier is to find a boundary between the source and the target samples, while the feature generator attempts to force target samples away from the boundary. Especially, for the target samples, the feature generator will determine whether to align them with source samples or reject them as unknown target samples. The experimental results have indicated the effectiveness of the proposed method.