CROSS-SCENE HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON CYCLE-CONSISTENT ADVERSARIAL NETWORKS

Lack of labeled training samples is a challenge in hyperspectral image (HSI) classification. Cross-scene classification is a valid solution to few-shot learning problem. In cross-scene classification, two strongly related HSI scenes are considered, one with sufficient labeled samples is called source scene, while the other one containing limited labeled samples is called target scene. By establishing connections between two scenes, abundant labeled samples in source scene can benefit the classification of target scene. In this paper, a novel model named cycle auxiliary classifier generative adversarial network (Cycle-AC-GAN) is proposed for heterogeneous transfer learning across source and target scenes. In Cycle-AC-GAN, a source-to-target generator and a target-to-source generator are simultaneously built. Thus, a two-way mapping can be effectively established between source and target scenes with the adversarial training. In addition, different from existing CycleGAN, in Cycle-AC-GAN, each discriminator contains a binary domain classifier and an auxiliary land-cover classifier. The auxiliary classifiers can align the class-conditional distributions between source and target HSIs. Inspiring experimental results on two real-world cross-scene HSI datasets demonstrate the effectiveness of the proposed approach.