A multiscale object detection approach for remote sensing images based on MSE-DenseNet and the dynamic anchor assignment. Issue 10 (3rd October 2019)

• Record Type: Journal Article
• Authors: Zhu, Huming; Zhang, Peng; Wang, Libing; Zhang, Xiaohua; Jiao, Licheng
• Is Part Of: Remote sensing letters. Volume 10:Issue 10(2019)
• Page Start: 959
• Page End: 967
• Publication Date: 2019-10-03
• Subjects: Remote sensing -- Periodicals; Remote sensing; Periodicals; 621.3678
• DOI: 10.1080/2150704X.2019.1633486
• Languages: English
• ISSNs: 2150-704X

Abstract

ABSTRACT: Object detection has played an important role in the remote sensing (RS) image understanding domain, but it still suffers from challenges, including the complexity of the image background, the variety of objects and differences of target scales. Although many studies applying deep learning methods to object detection have been presented, effective methods of multiscale object detection in RS images are still lacking. In this paper, we propose a novel object detection approach based on the Multiscale-SELU-DenseNet (MSE-DenseNet) and the dynamic anchor assignment (DAA) strategy. We fuse a highly modified DenseNet and a feature pyramid network into the MSE-DenseNet to extract multi-level feature descriptions of RS images, and then we use them to detect objects on various scales. To further improve the detection performance, we propose a DAA strategy for assigning prior anchor boxes. The experimental results show that our proposed method outperforms state-of-the-art detection methods on the VHR-10 dataset and maintains a real-time detection speed.