Representation Learning of Remote Sensing Knowledge Graph for Zero-Shot Remote Sensing Image Scene Classification

Abstract

Although deep learning has revolutionized remote sensing image scene classification, current deep learning-based approaches highly depend on the massive supervision of the predetermined scene categories and have disappointingly poor performance on new categories which go beyond the predetermined scene categories. In reality, the classification task often has to be extended along with the emergence of new applications that inevitably involve new categories of remote sensing image scenes, so how to make the deep learning model own the inference ability to recognize the remote sensing image scenes from unseen categories becomes incredibly important. By fully exploiting the remote sensing domain characteristic, this paper proposes a novel remote sensing knowledge graph-guided deep alignment network to address zero-shot remote sensing image scene classification. To improve the semantic representation ability of remote sensing-oriented scene categories, this paper, for the first time, tries to generate the semantic representations of remote sensing scene categories by representation learning of remote sensing knowledge graph (SR-RSKG). In addition, this paper proposes a novel deep alignment network with a series of constraints (DAN) to conduct robust cross-modal alignment between visual features and semantic representations. Extensive experiments on one merged remote sensing image scene dataset, which is the integration of multiple publicly open remote sensing image scene datasets, show that the presented SR-RSKG obviously outperforms the existing semantic representation methods (e.g., the natural language processing models and manually annotated attribute vectors), and our proposed DAN shows better performance compared with the state-of- the-art methods under different kinds of semantic representations.