Selection of Outline Descriptors Based on LightGBM with Application to Infrared Image Target Recognition

Infrared sensing technology can be well used for night observation, which is becoming an important measure for battlefield reconnaissance. It is a powerful way to implement precision strikes and situational awareness by improving the ability of target recognition based on infrared images. For the pr...

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Autores principales:	Xiaohong Hu, Ziyang Yao
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/d54597d6517e4d37a5936836bc143c41
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id	oai:doaj.org-article:d54597d6517e4d37a5936836bc143c41
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spelling	oai:doaj.org-article:d54597d6517e4d37a5936836bc143c412021-11-22T01:11:17ZSelection of Outline Descriptors Based on LightGBM with Application to Infrared Image Target Recognition1875-919X10.1155/2021/4940338https://doaj.org/article/d54597d6517e4d37a5936836bc143c412021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4940338https://doaj.org/toc/1875-919XInfrared sensing technology can be well used for night observation, which is becoming an important measure for battlefield reconnaissance. It is a powerful way to implement precision strikes and situational awareness by improving the ability of target recognition based on infrared images. For the problem of infrared image recognition, the Light Gradient Boosting Machine (LightGBM) is employed to select the outline descriptors extracted based on the elliptic Fourier series (EFS), which is combined with sparse representation-based classification (SRC) to achieve target recognition. First, based on the target outlines in the infrared image, the multi-order outline descriptors are extracted to characterize the essential characteristics of the target to be recognized. Then, the LightGBM feature selection algorithm is used to screen the multi-order outline descriptors to reduce redundancy and improve the pertinence of features. Finally, the selected outline descriptors are classified based on SRC. The method effectively improves the effectiveness of the final features through the feature selection of LightGBM and reduces the computational complexity of classification at the same time, which is beneficial to improve the overall recognition performance. The mid-wave infrared (MWIR) dataset of various targets is employed to carry out verification experiments for the proposed method under three different conditions of original samples, noisy samples, and partially occluded samples. By comparing the proposed method with several types of existing infrared target recognition methods, the results show that the proposed method can achieve better performance.Xiaohong HuZiyang YaoHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Xiaohong Hu Ziyang Yao Selection of Outline Descriptors Based on LightGBM with Application to Infrared Image Target Recognition
description	Infrared sensing technology can be well used for night observation, which is becoming an important measure for battlefield reconnaissance. It is a powerful way to implement precision strikes and situational awareness by improving the ability of target recognition based on infrared images. For the problem of infrared image recognition, the Light Gradient Boosting Machine (LightGBM) is employed to select the outline descriptors extracted based on the elliptic Fourier series (EFS), which is combined with sparse representation-based classification (SRC) to achieve target recognition. First, based on the target outlines in the infrared image, the multi-order outline descriptors are extracted to characterize the essential characteristics of the target to be recognized. Then, the LightGBM feature selection algorithm is used to screen the multi-order outline descriptors to reduce redundancy and improve the pertinence of features. Finally, the selected outline descriptors are classified based on SRC. The method effectively improves the effectiveness of the final features through the feature selection of LightGBM and reduces the computational complexity of classification at the same time, which is beneficial to improve the overall recognition performance. The mid-wave infrared (MWIR) dataset of various targets is employed to carry out verification experiments for the proposed method under three different conditions of original samples, noisy samples, and partially occluded samples. By comparing the proposed method with several types of existing infrared target recognition methods, the results show that the proposed method can achieve better performance.
format	article
author	Xiaohong Hu Ziyang Yao
author_facet	Xiaohong Hu Ziyang Yao
author_sort	Xiaohong Hu
title	Selection of Outline Descriptors Based on LightGBM with Application to Infrared Image Target Recognition
title_short	Selection of Outline Descriptors Based on LightGBM with Application to Infrared Image Target Recognition
title_full	Selection of Outline Descriptors Based on LightGBM with Application to Infrared Image Target Recognition
title_fullStr	Selection of Outline Descriptors Based on LightGBM with Application to Infrared Image Target Recognition
title_full_unstemmed	Selection of Outline Descriptors Based on LightGBM with Application to Infrared Image Target Recognition
title_sort	selection of outline descriptors based on lightgbm with application to infrared image target recognition
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/d54597d6517e4d37a5936836bc143c41
work_keys_str_mv	AT xiaohonghu selectionofoutlinedescriptorsbasedonlightgbmwithapplicationtoinfraredimagetargetrecognition AT ziyangyao selectionofoutlinedescriptorsbasedonlightgbmwithapplicationtoinfraredimagetargetrecognition
_version_	1718418310614220800

Selection of Outline Descriptors Based on LightGBM with Application to Infrared Image Target Recognition

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