Aircraft detection in remote sensing images based on deconvolution and position attention. Issue 11 (3rd June 2021)

• Record Type: Journal Article
• Authors: Shi, Lukui; Tang, Zhenjie; Wang, Tiantian; Xu, Xia; Liu, Jing; Zhang, Jun
• Is Part Of: International journal of remote sensing. Volume 42:Issue 11(2021)
• Page Start: 4241
• Page End: 4260
• Publication Date: 2021-06-03
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2021.1892858
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Motivated by the development of deep convolution neural networks (DCNNs), the aircraft detection from remote sensing images has gained tremendous progress. However, due to complex background and multi-scale characteristics, it remains a challenge in remote sensing detection. In this paper, we propose a two-stage aircraft detection method based on deep neural networks, which integrates Deconvolution operation with Position Attention mechanism (DPANet). Specifically, considering that remote sensing images are taken from the top-down perspective, which leads to significant external structural characteristic, we introduce a deconvolution module to capture the external structural feature representation of aircraft during the feature map generation process. Moreover, aiming at reducing the error detections caused by complex background in remote sensing, we propose a position attention module in the second stage. By calculating the feature similarity between any two pixels of the target feature map, DPANet can extract the complicated internal structure feature representation of aircraft, which improve the ability to distinguish background and aircraft. By integrating the deconvolution and position attention modules, DPANet can provide better representation ability for the structural characteristic of aircraft in remote sensing images. Experimental results show that the proposed method can effectively reduce the error detections and improve the accuracy of the aircraft detection.