Feature Enhanced Anchor-Free Network for School Detection in High Spatial Resolution Remote Sensing Images
Feature Enhanced Anchor-Free Network for School Detection in High Spatial Resolution Remote Sensing Images
复制标题
DOI：
10.3390/app12063114
复制
发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
H. Fu;Xiangtao Fan;Zhenzhen Yan;Xiaoping Du;Hongdeng Jian;Chen Xu
中科院分区：
文献类型：
--
作者：
H. Fu;Xiangtao Fan;Zhenzhen Yan;Xiaoping Du;Hongdeng Jian;Chen Xu
遥感图像中的目标检测是当前研究的重要课题之一，它可以促进人们对地球的认识，更好地为数字地球的建设服务。在遥感图像中，除了单个目标外，还存在着许多复合目标，尤其是中小学校，它们由多个部分组成，周围有着复杂的背景。现有的深度学习方法很难有效地检测复合对象。在这篇文章中，我们提出了一种基于无锚点方法的特征增强网络（FENet）用于PSS检测。FENet不仅可以基于增强的特征实现更准确的像素级检测，而且通过避免超参数来简化训练过程。首先，设计了一个增强的特征模块（EFM），以提高复杂特征的表示能力。其次，采用上下文感知策略来减轻背景信息的干扰。此外，完全交并（CIoU）损失的边界盒回归，可以获得更好的收敛速度和精度。同时，我们建立了一个用于复合目标检测的PSSs数据集。该数据集包含1685张京津冀地区的PSS图像。实验结果表明，FENet优于几个目标检测器，达到78.7%的平均精度。研究表明，我们所提出的方法的优势，PSS检测。
Object detection in remote sensing images (RSIs) is currently one of the most important topics, which can promote the understanding of the earth and better serve for the construction of digital earth. In addition to single objects, there are many composite objects in RSIs, especially primary and secondary schools (PSSs), which are composed of some parts and surrounded by complex background. Existing deep learning methods have difficulty detecting composite objects effectively. In this article, we propose a feature enhanced Network (FENet) based on an anchor-free method for PSSs detection. FENet can not only realize more accurate pixel-level detection based on enhanced features but also simplify the training process by avoiding hyper-parameters. First, an enhanced feature module (EFM) is designed to improve the representation ability of complex features. Second, a context-aware strategy is used for alleviating the interference of background information. In addition, complete intersection over union (CIoU) loss is employed for bounding box regression, which can obtain better convergence speed and accuracy. At the same time, we build a PSSs dataset for composite object detection. This dataset contains 1685 images of PSSs in Beijing–Tianjin–Hebei region. Experimental results demonstrate that FENet outperforms several object detectors and achieves 78.7% average precision. The study demonstrates the advantage of our proposed method on PSSs detection.