Sensor-Specific Adversarial Network for Transferable Land-Cover Classification

2021 
As the multi-source high-spatial-resolution (HSR) images are being daily acquired from different sensors, it brings the challenge of transferring the recognition model from labeled images to new unlabelled images obtained from other sensors. Existing deep transfer learning methods encode the land-cover features in the same architecture, which ignores the sensor divergence. In this paper, we tackle this problem by proposing a sensor-specific adversarial network for HSR land-cover classification. Specifically, the sensor-specific normalization (SN) is designed for decoupling the sensor divergence in different normalization weights. Moreover, the transferable adversarial optimization is proposed for effectively optimizing the source-related, target-related, and discriminator weights. Considering the sensor-specific characteristics, our proposed method improves the transferability of deep learning models between airborne and spaceborne sensors. The mutual transferability experiments on a self-constructed cross-sensor land-cover dataset demonstrate that the proposed method outperforms the state-of-the-art deep transfer learning methods.
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