Stepwise Locating Bidirectional Pyramid Network for Object Detection in Remote Sensing Imagery

• Published in: IEEE geoscience and remote sensing letters Vol. 20; pp. 1 - 5
• Main Authors: Yu, Nanjing, Ren, Haohao, Deng, Tianmin, Fan, Xiaobiao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aggregation; Channel attention; Convolution; convolutional neural network (CNN); Detection; Feature extraction; Image resolution; Locating; Neural networks; Object detection; Object recognition; Optical imaging; Optical sensors; Remote sensing; remote sensing image
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2022.3223470

Recently, optical object detection has made significant advancements in the field of remote sensing. However, small-scale object detection is still a major challenge in optical remote sensing image interpretation. Therefore, this letter proposed a novel object detection method called stepwise locating bidirectional pyramid network (Sw-LBPN) to heighten the ability of remote sensing image object detection. Precisely, a stepwise locating attention scheme is proposed to highlight useful information and suppress useless ones of objects step by step at the feature channel level for large-scale remote sensing images. To effectively realize multiscale feature aggregation, a simplified bidirectional feature pyramid network (SBFPN) is designed. Moreover, the skip connection is leveraged in the middle level of SBFPN, aiming at offsetting and reusing small-scale object information. Several experiments on the measured object detection in optical remote sensing images (DIOR) and Northwestern Polytechnical University very high resolution 10-class remote sensing images (NWPU VHR-10) datasets demonstrate the effectiveness and the superiority of the proposed method compared with some state of the arts.