Synthetic Aperture Radar Automatic Target Recognition Based on a Simple Attention Mechanism
Autor:
Ukwuoma, Chiagoziem Chima
; Zhiguang, Qin
; Tienin, Bole W.
; Yussif, Sophyani B.
; Ejiyi, Chukwuebuka Joseph
; Urama, Gilbert C.
; Ukwuoma, Chibueze D.
; Chikwendu, Ijeoma Amuche
Fecha:
12/2023Palabra clave:
Revista / editorial:
International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)Tipo de Ítem:
articleDirección web:
https://www.ijimai.org/journal/bibcite/reference/3265Resumen:
A simple but effective channel attention module is proposed for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). The channel attention technique has shown recent success in improving Deep Convolutional Neural Networks (CNN). The resolution of SAR images does not surpass optical images thus information flow of SAR images becomes relatively poor when the network depth is raised blindly leading to a serious gradients explosion/vanishing. To resolve the issue of SAR image recognition efficiency and ambiguity trade-off, we proposed a simple Channel Attention module into the ResNet Architecture as our network backbone, which utilizes few parameters yet results in a performance gain. Our simple attention module, which follows the implementation of Efficient Channel Attention, shows that avoiding dimensionality reduction is essential for learning as well as an appropriate cross-channel interaction can preserve performance and decrease model complexity. We also explored the One Policy Learning Rate on the ResNet-50 architecture and compared it with the proposed attention based ResNet-50 architecture. A thorough analysis of the MSTAR Dataset demonstrates the efficacy of the suggested strategy over the most recent findings. With the Attention-based model and the One Policy Learning Rate-based architecture, we were able to obtain recognition rate of 100% and 99.8%, respectively.
Ficheros en el ítem
Este ítem aparece en la(s) siguiente(s) colección(es)
Estadísticas de uso
Año |
2012 |
2013 |
2014 |
2015 |
2016 |
2017 |
2018 |
2019 |
2020 |
2021 |
2022 |
2023 |
2024 |
Vistas |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
86 |
133 |
Descargas |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
192 |
75 |
Ítems relacionados
Mostrando ítems relacionados por Título, autor o materia.
-
Comparative Analysis of Building Insurance Prediction Using Some Machine Learning Algorithms
Ejiyi, Chukwuebuka Joseph; Qin, Zhen; Salako, Abdulhaq Adetunji; Happy, Monday Nkanta; Nneji, Grace Ugochi; Ukwuoma, Chiagoziem Chima; Chikwendu, Ijeoma Amuche; Gen, Ji (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 03/2022)In finance and management, insurance is a product that tends to reduce or eliminate in totality or partially the loss caused due to different risks. Various factors affect house insurance claims, some of which contribute ... -
A deep learning architecture for power management in smart cities
Xin, Qin; Alazab, Mamoun; García Díaz, Vicente; Montenegro-Marin, Carlos Enrique; González-Crespo, Rubén (Elsevier Ltd, 2022)Sustainable energy management is an inexpensive approach for improved energy use. However, the research used does not focus on cutting-edge technology possibilities in an Internet of things (IoT). This paper includes the ... -
AI-based quality of service optimization for multimedia transmission on Internet of Vehicles (IoV) systems
Xin, Qin; Alazab, Mamoun; González-Crespo, Rubén ; Montenegro-Marin, Carlos Enrique (Sustainable Energy Technologies and Assessments, 2022)Multimedia Communications of Internet of Vehicles (IoV) uses WLAN, NFC and Fifth Generation networks. At the same time, in multimedia communications in healthcare, IoV's essential task is optimizing the quality of experience ...