Automatic target recognition on land using three dimensional (3D) laser radar and artificial neural networks

Goztepe, Kerim

doi:10.7166/24-1-650

Cited by 1 publication

(2 citation statements)

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“…The ability to identify and recognize ground targets quickly and accurately enables an army to dominate battlefields and recognize the movements and intention of enemies (Yoo& Kim, 2021). Accurate detection, recognition, and classification of military vehicles as a target in a theatre of operation from an unmanned aerial vehicle is an essential strategy used by contemporary armies to minimize casualties and increase military intelligence (Goztepe, 2013). Hence, system is required to minimize the role of humans in target detection, recognition and classification, in order to implement a real-time and reliable system with high performance accuracy (Goztepe, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Accurate detection, recognition, and classification of military vehicles as a target in a theatre of operation from an unmanned aerial vehicle is an essential strategy used by contemporary armies to minimize casualties and increase military intelligence (Goztepe, 2013). Hence, system is required to minimize the role of humans in target detection, recognition and classification, in order to implement a real-time and reliable system with high performance accuracy (Goztepe, 2013). SAR systems have been used in many civil and military applications, including terrain mapping, geological exploration, marine observation, atmospheric and environmental investigation, and automatic target recognition (Wang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Multi-Class Dual- Stream Convolutional Neural Network for Synthetic Aperture Radar Automatic Target Recognition of Ground Military Vehicle

Awujoola¹,

Odion²,

Evwiekpaefe³

et al. 2022

JSR

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Synthetic Aperture Radar (SAR) target classification is one of the largest branches of SAR image analysis. Despite the remarkable achievements of deep learning-based SAR target prediction algorithms, current object recognition algorithms are limited in terms of military applications. Acquisition and labeling of SAR target images are time-consuming and cumbersome. Obtaining adequate training data is also challenging in many cases. Deep learning-based models are always susceptible to overfitting because of insufficient training data. This limitation prevents them from being widely used to classify SAR targets. To overcome the problem of insufficient sampling and to learn more accurate representations for SAR image recognition, we propose a two way input of SAR images into a dual stream of DCNN. Concatenating two input SAR images representations was done using the restricted raw SAR data in order to extract the integral features from the 2 input SAR images representations for classification. The proposed methodology addressed the problem of insufficient sample in SAR target classification and improved classification accuracy without overfitting. Experimental results confirmed that the proposed method is effective in addressing the problem of insufficient sample in SAR target classification. This technique can be integrated into any SAR classification model based on convolutional neural networks (CNNs). The model MCDS-CNN results in a 99.1% recognition accuracy. Despite the limited availability of SAR image data from MSTAR, this approach provides good recognition results.

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Section: Introductionmentioning

confidence: 99%