2021
DOI: 10.1109/jstars.2021.3115637
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Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Abstract: Factors, such as rapid relative motion, clutter background, etc., make robust small aerial target detection for airborne infrared detection systems a challenge. Existing methods are facing difficulties when dealing with such cases. We consider that a continuous and smooth trajectory is critical in boosting small infrared aerial target detection performance. A simple and effective small aerial target detection method for airborne infrared detection system using LightGBM and trajectory constraints is proposed in… Show more

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Cited by 32 publications
(10 citation statements)
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“…In order to assess the effectiveness of the proposed algorithm, the study employed a variety of classification algorithms for comparison. The baseline algorithms included traditional machine learning techniques like SVM [35], RF [35], CRC, and ProCRC [36] , while more advanced ensemble methods, such as GBDT [37] and LightGBM [38], [39] ], were also considered. Additionally, the paper utilized the META-DES [40]- [42]algorithm as an advanced comparative approach.…”
Section: B Experiments Setupmentioning
confidence: 99%
“…In order to assess the effectiveness of the proposed algorithm, the study employed a variety of classification algorithms for comparison. The baseline algorithms included traditional machine learning techniques like SVM [35], RF [35], CRC, and ProCRC [36] , while more advanced ensemble methods, such as GBDT [37] and LightGBM [38], [39] ], were also considered. Additionally, the paper utilized the META-DES [40]- [42]algorithm as an advanced comparative approach.…”
Section: B Experiments Setupmentioning
confidence: 99%
“…The light gradient boosting machine (LGBM) [48] is widely used in the field of remote sensing, and research has proved that the LGBM has obvious advantages in computational speed and accuracy compared with other similar algorithms [49,50]. Given the advantages of machine learning in nonlinear mapping, we used the LGBM regression model to obtain the weights of each impact factor by nonlinearly fitting the bias pairs.…”
Section: Nonlinear Fitting Of Image Bias Pairsmentioning
confidence: 99%
“…For example, the classic machine learning algorithms support vector (SVM) random forest (RF) are the baselines. Moreover, the advanced ensemble algorithm GBDT, Catboost [50], LightGBM [51], and XGboost [52] are also used as a comparison algorithm. In addition, the two state-of-the-art DES algorithms, namely DES-MI and META-DES algorithm are used as comparative algorithms in the paper.…”
Section: A Experiments Setupmentioning
confidence: 99%