A Discriminative Distillation Network for Cross-Source Remote Sensing Image Retrieval

Nowadays, several remote sensing image capturing technologies are used ranging from unmanned aerial vehicles to satellites. Powerful learning-based discriminative features play an essential role in content-based remote sensing image retrieval (CBRSIR). Cross-source CBRSIR (CS-CBRSIR) is used to find relevant remote sensing images across different remote sensing sources (i.e., multispectral images and panchromatic images). But it is limited by large cross-source and intrasource variations caused by different semantic objects, spatial resolution, and spectral resolution. The main limitation of CS-CBRSIR is that it cannot address the inconsistency between different sources and exploit the intrinsic relation between them. This study proposes a discriminative distillation network for CS-CBRSIR to address this limitation. To enlarge the interclass variations and reduce the intraclass differences, the discriminative features from the first source are first extracted with a well-designed joint optimization configuration (JOC) on the basis of deep neural networks. Thereafter, the features extracted from the first source are used as a supervision signal for the second source; feature distribution in common feature space between the first and second sources are made significantly similar. The method proposed in this study simultaneously handles the cross-source and intersource variations, unlike the existing methods. Extensive experiments on the DSRSID dataset with Euclidean distance verify the effectiveness of our proposed method.