Hierarchical Heterogeneous Geometric Foreground Perception Network for Remote Sensing Object Detection

Dong Ren, Yang Liu, Hang Sun, Lefei Zhang, Jun Wan

Abstract: Recently, deep learning-based remote sensing object detection (RSOD) has been widely explored and obtained remarkable performance. However, most existing multiscale feature extraction methods neglect exploring the interfering representation of different hierarchical features in the backbone, which is crucial for learning more discriminative features. Moreover, feature pyramid network (FPN) and its variants have difficulty in effectively perceiving the pose and salient information of remote sensing objects, leading to reduced detection accuracy. To address these issues, we propose a hierarchical heterogeneous geometric foreground perception network (HHGFP-Net) for RSOD. Specifically, a hierarchical heterogeneous receptive-field module (HHRM) is proposed to reward and penalize the feature information of the corresponding levels according to the differences between the shallow and deep feature layers in the backbone, improving discriminative feature ability. Furthermore, a geometric foreground perception FPN (GFP-FPN) is developed to refine geometric shapes and enhance foreground contents, providing more precise feature representations for objects, particularly small objects. Experimental results on four challenging RSOD datasets demonstrate that our HHGFP-Net achieves state-of-the-art performance. Codes are available at: https://github.com/YyLinkWorld/HHGFP-Net.

External IDs:doi:10.1109/tgrs.2025.3541220