Target Registration Based on Fusing Features of Visible and Two Wave Bands Infrared Images

Junhua Yan; Kai Su; Xuyang Cai; Tianxia Xie; Yin Zhang; Kun Zhang

Artificial Intelligence for Communications and Networks. Second EAI International Conference, AICON 2020, Virtual Event, December 19-20, 2020, Proceedings

Research Article

Target Registration Based on Fusing Features of Visible and Two Wave Bands Infrared Images

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@INPROCEEDINGS{10.1007/978-3-030-69066-3_41,
    author={Junhua Yan and Kai Su and Xuyang Cai and Tianxia Xie and Yin Zhang and Kun Zhang},
    title={Target Registration Based on Fusing Features of Visible and Two Wave Bands Infrared Images},
    proceedings={Artificial Intelligence for Communications and Networks. Second EAI International Conference, AICON 2020, Virtual Event, December 19-20, 2020, Proceedings},
    proceedings_a={AICON},
    year={2021},
    month={7},
    keywords={Target registration Fusion VIS image LWIR image MWIR image Improved structural similarity},
    doi={10.1007/978-3-030-69066-3_41}
}

Junhua Yan
Kai Su
Xuyang Cai
Tianxia Xie
Yin Zhang
Kun Zhang
Year: 2021
Target Registration Based on Fusing Features of Visible and Two Wave Bands Infrared Images
AICON
Springer
DOI: 10.1007/978-3-030-69066-3_41

Junhua Yan¹, Kai Su², Xuyang Cai², Tianxia Xie², Yin Zhang¹, Kun Zhang²

1: Key Laboratory of Space Photoelectric Detection and Perception, Nanjing University of Aeronautics and Astronautics, Ministry of Industry and Information Technology
2: Nanjing University of Aeronautics and Astronautics

Abstract

In order to register the same target in images from different sources to improve the accuracy of target recognition of multi-source images, based on the principle that the same target has the highest similarity among targets in these images, this paper proposes a new target registration algorithm by fusing features of Visible (VIS), Long Wave Infrared (LWIR) and Middle Wave Infrared (MWIR) images, which registers the same target in these images by calculating the targets similarity in different source images. Firstly, the similarity between targets in LWIR and MWIR images is calculated by using the improved structural similarity. Then, the similarity between targets in VIS and LWIR images is calculated by using Hu invariant moment feature and cosine similarity. Finally, the similarity among targets in VIS, MWIR and LWIR images is calculated by fusing these two kinds of target similarity, so that target registration of these three-source images is realized. Experimental results show that the proposed algorithm has high correct rate and accuracy of target registration. Specifically, the correct rate of target registration is 83.87% and the accuracy of target registration is higher than 0.95.

Keywords: Target registration Fusion VIS image LWIR image MWIR image Improved structural similarity

Published: 2021-07-16
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-030-69066-3_41