SRODET: Semi-Supervised Remote Sensing Object Detection With Dynamic Pseudo-Labeling

Abstract

To mitigate the impact of noisy labels, many methods prioritize simple samples with reliable labels, often overlooking the valuable information in more challenging samples. This study introduces SRODET, a novel semi-supervised remote sensing object detection model that leverages sample complexity to extract accurate pseudo-labeled knowledge. We employ a dual-branch structure (DBS) to generate reliable pseudo labels for auxiliary supervision, enhancing joint supervision to derive high-quality pseudo labels from low-confidence predictions. This approach reduces the risk of losing object instances due to low-confidence scores, particularly for extreme scales. Additionally, we introduce a pseudo-label training strategy based on sample difficulty, evaluating complexity through object uncertainty and angular information from remote sensing images. Our experimental results show that SRODET achieves state-of-the-art performance in semi-supervised remote sensing object detection across various settings in the DOTA-v1.5 and HRSC2016 benchmarks.