Open AccessClassification of Aircraft in Remote Sensing Images Based on Deep Convolutional Neural Networks
Author(s) -
Youssef Ben Youssef,
Mohamed Merrouchi,
Elhassane Abdelmounim,
Taoufiq Gadi
Publication year - 2022
Publication title -
statistics, optimization and information computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.297
H-Index - 12
eISSN - 2311-004X
pISSN - 2310-5070
DOI - 10.19139/soic-2310-5070-1143
Subject(s) - convolutional neural network , preprocessor , computer science , artificial intelligence , deep learning , component (thermodynamics) , pattern recognition (psychology) , image (mathematics) , remote sensing , contextual image classification , computer vision , geology , physics , thermodynamics
Convolutional Neural Network (CNN) is a component of Deep Learning(DL) recently exploited in different fifields. In this work, we improve the performance of multi-label classifification based on CNN for remote sensing images of aircraft types. Intensive preprocessing limits the classifification rate in previous studies. In order to avoid under-fifitting and over-fifitting problems, we optimized the architecture and Network parameters. To validate our method the recent public image dataset called Multi-Type Aircraft Remote Sensing Images (MTARSI) is used. Extensive experiments prove the effectiveness of the proposed method in terms of classifification rate.