@inproceedings{a38b7cb3cb694cff9257094defc93bb6,
title = "Heterogeneous Image Fusion for Target Recognition Based on Evidence Reasoning",
abstract = "Multi-source fusion is an efficient strategy in complex image target recognition since it can exploit the complementary knowledge in different sources to improve the classification performance. In this paper, we propose a new end-to-end framework for heterogeneous (i.e. visible & infrared) image fusion target recognition (HIFTR). Firstly, two networks are built for the visible and infrared images respectively and jointly trained based on mutual learning. It aims to transfer heterogeneous information mutually and improve the generalization performance of the networks. Secondly, a weighted decision-level fusion method based on evidence reasoning is developed to combine the classification results of visible and infrared images for the final target recognition. In the training process, the weight of each image is automatically optimized in the networks. Finally, the performance of the proposed HIFTR has been evaluated by comparing with other related methods, and the experimental results show that the HIFTR method can efficiently improve the classification accuracy.",
keywords = "Evidence reasoning, Heterogeneous image fusion, Mutual learning, Target recognition",
author = "Shuyue Wang and Zhunga Liu and Zuowei Zhang and Yang Li",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 7th International Conference on Belief Functions, BELIEF 2022 ; Conference date: 26-10-2022 Through 28-10-2022",
year = "2022",
doi = "10.1007/978-3-031-17801-6_15",
language = "英语",
isbn = "9783031178009",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "153--162",
editor = "{Le H{\'e}garat-Mascle}, Sylvie and Emanuel Aldea and Isabelle Bloch",
booktitle = "Belief Functions",
}