Domain Adaptive Oriented Object Detection From Optical to SAR Images

Abstract

Oriented object detection in synthetic aperture radar (SAR) images presents significant challenges due to the scarcity of labeled data. In contrast, acquiring labeled optical remote sensing images is considerably easier. This article proposes a domain adaptive oriented object detection (DAOOD) model, termed the pixel-instance information transfer-based model (PITM). PITM aims to transfer knowledge from optical to SAR domains, thereby reducing the dependency of oriented SAR object detection on labels. Given the pronounced domain disparity between optical and SAR images, the efficient migration of both visual content and rotating instances is incorporated to bridge the gap in their information distribution simultaneously. Specifically, regarding pixel-level information transfer, speckle noise from SAR images is mixed into the optical domain to form an intermediate domain, thus compensating for the visual difference between the two domains. For instance-level information transfer, considering the angle diversity of rotating objects, multiscale and multidirectional spatial information extraction is combined with decoupled instance-invariant features, enhancing the cross-domain discernment capacity of rotating instances. Experimental results on four DAOOD benchmarks (i.e., two optical datasets to two SAR datasets) demonstrate that the proposed PITM significantly improves oriented object detection performance, even in the absence of labeled SAR images. Specifically, it individually outperforms two source-only models by 88.16% and 54.62% in average precision (AP).