Automatic target detection and recognition of military vehicles in synthetic aperture radar images is fostered by optimizing VGG-googLeNet with the giraffe kicking optimization algorithm.

Autor: Shakin Banu, A., Shahul Hameed, K. A.
Zdroj: Signal, Image & Video Processing; Sep2024, Vol. 18 Issue 8/9, p6491-6502, 12p
Abstrakt: Artificial Aperture Radar (SAR) Multiview pictures have the ability for providing far more detailed information than single-view images for automatic target recognition. Acquiring a series of SAR images from suitable viewpoints and determining the best SAR platform flight routes are required to perform Multiview SAR ATR precisely and effectively. In this paper, Automatic Target Detection and Recognition of Military Vehicles in Synthetic Aperture Radar Images is fostered by optimizing VGG-GoogLeNet with the Giraffe Kicking Optimization Algorithm (VGG-GNet-TDMV-SAR) is proposed. Initially, SAR images are taken from the Military and Civilian Vehicles Classification dataset. Then the input SAR image is pre-processing using Adaptive Multi scale Gaussian Co-Occurrence Filtering for eliminating speckle noise present in the SAR imageries. The Hesitant Fuzzy Linguistic Bi-objective Clustering technique is utilized to extract Region of Interest after pre-processing. The extracted SAR image is given to Target Detection using Integrated Fuzzy Decision Tree. After that, VGG-Google Net (VGG-GNet) classifier classifies the military and non-military vehicle. In general, VGG-GNet does not adapt any optimization techniques to define the optimal parameter and to ensure the precise detection. So, Giraffe Kicking Optimization Algorithm is proposed to optimize VGG-GNet classifier's weight parameters. The proposed technique is implemented using MATLAB and analyzed with performance metrics. The proposed technique achieves greater accuracy, lower computation time, greater ROC compared with existing techniques. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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