Cross-Modal Remote Sensing Image Retrieval Via Intra- and Inter-Modal Feature Matching

Abstract

With the development of remote sensing (RS) acquisition technology, a mass of RS images have been produced, which brings challenges to the traditional manual retrieval methods and gives birth to the automatic RS image retrieval methods. Cross-modal RS image retrieval allows the usage of text and other modalities to retrieve RS images. For its flexible and convenient advantages, it has become a research hotspot. However, cross-modal RS image retrieval encounters the information asymmetry between modalities, i.e., RS images possess multi-scale, multi-objective properties and own rich information. At the same time, the query text is usually short and with less information. To solve the issues above, a cross-modal feature matching network is proposed to learn the feature fusion intra-modalities and the feature association inter-modalities to avoid the poor retrieval performance caused by the information asymmetry. Specifically, for the feature fusion intra-modalities, relying on the powerful feature representation ability of graph network, text and RS image graph modules are designed to fuse the intra-modal features. In terms of the feature correlation between modalities, RS image-text association module is created to attend the parts in text related to RS images and vice versa. Extended experiments on two public standard datasets verify the effectiveness of the proposed model.