Object Detection in Remote Sensing Images Based on a Scene-Contextual Feature Pyramid Network

Remote Sensing

Weihong Li¹  Weiguo Gong¹  Yongliang Chen²  Chaoyue Chen² 

[1] Systems of Education Ministry, Chongqing University, Chongqing 400044, China;;Key Lab of Optoelectronic Technology &

关键词: convolutional neural network (CNN);    object detection;    remote sensing images;    scene-contextual feature pyramid network (SCFPN);   

DOI  :  10.3390/rs11030339

来源: DOAJ

【 摘 要 】

Object detection has attracted increasing attention in the field of remote sensing image analysis. Complex backgrounds, vertical views, and variations in target kind and size in remote sensing images make object detection a challenging task. In this work, considering that the types of objects are often closely related to the scene in which they are located, we propose a convolutional neural network (CNN) by combining scene-contextual information for object detection. Specifically, we put forward the scene-contextual feature pyramid network (SCFPN), which aims to strengthen the relationship between the target and the scene and solve problems resulting from variations in target size. Additionally, to improve the capability of feature extraction, the network is constructed by repeating a building aggregated residual block. This block increases the receptive field, which can extract richer information for targets and achieve excellent performance with respect to small object detection. Moreover, to improve the proposed model performance, we use group normalization, which divides the channels into groups and computes the mean and variance for normalization within each group, to solve the limitation of the batch normalization. The proposed method is validated on a public and challenging dataset. The experimental results demonstrate that our proposed method outperforms other state-of-the-art object detection models.

【 授权许可】

Unknown