Aircraft detection in remote sensing images based on deconvolution and position attention

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.

Acknowledgements

The authors would be grateful to the anonymous reviewers for their very helpful suggestions for improving this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported in part by the Natural Science Foundation of Hebei Province (Grant No. F2019202062), and in part by the Tianjin Science and Technology Project (Grant No. 18YFCZZC00060 and No. 18ZXZNGX00100), and in part by the National Natural Science Foundation of China (Grant No. 62001251 and Grant No. 62001252), and in part by the China Postdoctoral Science Foundation (Grant No. 2020M670631).