OGSOD-2.0: a challenging multimodal benchmark for optical-SAR object detection

With the development of the rapid development of satellite technology, multimodal object detection between optical and SAR (Synthetic Aperture Radar) images has attracted growing interest in the field of remote sensing image interpretation. However, this area lacks a dataset that considers novel and diverse challenges related to practical applications, crucial for both the training and evaluation of recent detectors. In this paper, we introduce a new challenging multi-modal dataset for optical-SAR object detection, OGSOD-2.0, aiming to enhance object detection in tiny-scale and crowded objects under complex backgrounds. Building on OGSOD-1.0, the proposed dataset supplements more 5,130 optical-SAR image pairs from the Sentinel satellite series with 24,421 instance annotations, containing four significant types of static objects: bridges, harbors, oil tanks, and playgrounds. These objects exhibit a wide variety of scales, aspect ratios, and orientations under complex aerial scenarios. Specially, most objects are characterized by relatively low resolution, even smaller than 12 pixels, and clustered together at high densities, further increasing the challenges for existing detection methods. To evaluate the challenging aspects of OGSOD-2.0, we compare our proposed dataset with existing optical-SAR datasets over several state-of-the-art methods including single-modal, cross-modal and multimodal detectors. Comprehensive experiments show that proposed OGSOD-2.0 are quite challenging and related to practical applications. This multi-modal benchmark will be publicly available.