LAUSR

Over the past decades, the successful invention and employment of multiple sensors have marked the advent of a new era in multisensor remote-sensing (RS) images acquisition. To effectively utilize the massive multisensor images for RS scene understanding, we expect that a scene classification model learned with particular sensor data can generalize well to other sensor data. However, this is a very challenging task due to the cross-sensor data differences. In the deep learning (DL) pipeline, a common way to handle this challenging task is to fine-tune the models pretrained on source sensor data with limited labeled data from the target sensor. Unfortunately, fine-tune technique is usually applied between homogeneous networks, which may not be the best choice if the source and target data are largely different. To address these issues, we formulate the cross-sensor RS scene understanding problem as a heterogeneous network-oriented transfer learning problem, in which the source and the target networks are different and data-oriented selected. Afterward, the knowledge between heterogeneous networks is transferred using the pseudo-label recursive propagation mechanism inspired by the concept of knowledge distillation. To the best of our knowledge, this is the first time to investigate the cross-sensor scene classification problem by constructing such a heterogeneous networks’ transfer scheme in RS fields. Our experiments using two cross-sensor RS datasets [aerial images $\rightarrow $ multispectral images (MSIs) and aerial images $\rightarrow $ hyper-spectral images (HSIs)] demonstrated that the proposed transfer learning strategy based on heterogeneous networks outperforms the supervised learning (SL) and fine-tune scheme for cross-sensor scene classification.