Heterogeneous Object-Level Aircraft Change Detection via Cross-Modal Interaction and Imbalanced Learning

Heterogeneous object-level change detection (CD) aims to detect the state of the objects and whether they have changed from multitemporal multimodal data. In this article, a new cross-modality interactive change detector (CICD) is proposed for object-level CD from multitemporal optical and synthetic aperture radar (SAR) images. The CICD consists of a backbone, cross-modal interactive module (CIM), neck, and head. CIM is designed to work with features extracted from modalities by the backbone network, enabling it to identify more changes in objects. Moreover, to address data imbalances in change categories caused by variations in satellite revisit cycles and aircraft flight plans, we introduce a heterogeneous class balanced module (HCBM). An eliminate adversarial network (EAN) is constructed as the main component of the HCBM. It is used to eliminate objects to augment images in which objects appear in only one temporal instant, thus reducing imbalances in the dataset. Extensive experiments are conducted on the multimodal object-level change dataset (MOCD), and the results show that CICD can achieve state-of-the-art performance.