Region of Interest Extraction Based on Unsupervised Cross-Domain Adaptation for Remote Sensing Images

Extracting region of interest (ROI) plays an important role in many computer vision tasks. Recently, deep methods have shown excellent performance, however, when it comes to remote sensing image (RSI) domain, which lacks pixel-level annotations, training often leads to under-fitting and low-accuracy. In this paper, we propose a novel ROI extraction model based on unsupervised cross-domain adaptation for RSIs. Firstly, we pretrain the network, RS- RoINet, by large-scale natural datasets to learn general features. Through top-down propagation mechanism, we combine global and local information to generate the accurate edge of extraction maps. Then, we introduce domain adaptation module to reduce the difference between natural domain and RSI domain. Data from both domains is transferred into Reproducing Kernel Hilbert Space to measure the domain distribution distance. Finally, the model is adaptive for RSIs and extracts ROI more accurately. Compared with recent fully-supervised state-of-the-arts, our unsupervised method shows outstanding performance.