Cross-Source Image Retrieval Based on Ensemble Learning and Knowledge Distillation for Remote Sensing Images

As different kinds of high-resolution remote sensing (HRRS) image data sources increase, the cross-source content-based image retrieval (CS-CBRSIR) is becoming an important and urgent task to be solved. Most existing methods focus on optimizing the common space features for dual-source effectively. The source discrepancy in classifier level, however, has been ignored. To handle this problem, we propose teacher-ensemble learning with the knowledge distillation method in this paper. The ensemble of source-shared and source-specific classifiers could construct an effective teacher model. The useful information can be transferred back with the knowledge distillation. Besides, the feature pyramid network is introduced to learn the multi-scale features from HRRS images, which can describe the complex contents of HRRS images well. The positive experimental results conducted on DSRSID illustrates the effectiveness of the proposed method.