Optimized Data Distribution Learning for Enhancing Vision Transformer‐Based Object Detection in Remote Sensing Images

作者：Huaxiang Song, Junping Xie, Yunyang Wang, Lihua Fu, Yang Zhou, Xing Zhou · 发表于：The Photogrammetric Record · 年份：2025 · DOI：10.1111/phor.70004 · 被引用次数：32 · 研究领域：Remote-Sensing Image Classification、Infrared Target Detection Methodologies、Advanced Image and Video Retrieval Techniques

ABSTRACT Existing Vision Transformer (ViT)‐based object detection methods for remote sensing images (RSIs) face significant challenges due to the scarcity of RSI samples and the over‐reliance on enhancement strategies originally developed for natural images. This often leads to inconsistent data distributions between training and testing subsets, resulting in degraded model performance. In this study, we introduce an optimized data distribution learning (ODDL) strategy and develop an object detection framework based on the Faster R‐CNN architecture, named ODDL‐Net. The ODDL strategy begins with an optimized augmentation (OA) technique, overcoming the limitations of conventional data augmentation methods. Next, we propose an optimized mosaic algorithm (OMA), improving upon the shortcomings of traditional Mosaic augmentation techniques. Additionally, we introduce a feature fusion regularization (FFR) method, addressing the inherent limitations of classic feature pyramid networks. These innovations are integrated into three modular, plug‐and‐play components—namely, the OA, OMA, and FFR modules—ensuring that the ODDL strategy can be seamlessly incorporated into existing detection frameworks without requiring significant modifications. To evaluate the effectiveness of the proposed ODDL‐Net, we develop two variants based on different ViT architectures: the Next ViT (NViT) small model and the Swin Transformer (SwinT) tiny model, both used as detection backbones. Experimental results...