Object Detection from Aerial Multi-Angle Thermal Infrared Remote Sensing Images: Dataset and Method

发布时间： 2025-08-06 点击次数：

• 影响因子：12.2
• DOI码：10.1016/j.isprsjprs.2025.07.024
• 发表刊物：ISPRS Journal of Photogrammetry and Remote Sensing
• 关键字：Thermal Infrared Remote Sensing Multiple Angles Object Detection
• 摘要：Multi-angle thermal infrared (MATIR) remote sensing provides valuable day-night and multiple angular information that is of significant practical value in applications. However, multi-angle data heterogeneity is one of the core challenges in feature learning and scene understanding, which could severely degrade the model inference performance of deep neural networks. To address this issue, this study proposes a new fine-grained dataset and a unified method for the MATIR object detection task. In detail, the fine-grained MATIR object detection (MATIR-OD) dataset is captured by an unmanned aerial vehicle (UAV)-based platform, which offers significant advantages in terms of cost efficiency and exceptional maneuverability. The MATIR-OD dataset comprises 24 fine-grained and multi-angle data subsets, containing a total of 43,540 instances. Moreover, the unified MATIR object detection method, denoted as U-MATIR, includes the heterogeneous label space module and hybrid view cascade module. In the multi-angle object detection task, based on four public datasets and the proposed dataset, the all-angle experimental results show that the U-MATIR outperforms the ground- or aerial-view object detection models, increasing accuracy with an approximately 18–65% improvement in the mean Averaged Precision (mAP) metric, which exhibits notable robustness and generalization ability. In addition, the extensive experiments demonstrate the boundaries of robustness and generalization ability under 20–120 m and 30–90° fine-grained observation data. In particular, the optimal detection angle is defined as 60° under the above observation heights. The MATIR object detection dataset and unified method provide new insight for accurate multi-angle localization and achieve competitive detection performance.
• 论文类型：期刊论文
• 学科门类：工学
• 一级学科：测绘科学与技术
• 文献类型：J
• 卷号：228
• 页面范围：438-452
• 是否译文：否
• 收录刊物：SCI、EI
• 发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0924271625002862
• 第一作者：Chenchen Jiang
• 通讯作者：Huazhong Ren
• 全部作者：Fengguang Li,Zhonghua Hong,Hongtao Huo,Junqiang Zhang,Jiuyuan Xin
• 发表时间：2025-07-17