STATISTICS, OPTIMIZATION AND INFORMATION COMPUTING Stat., Optim. Inf. Comput., Vol. 10, February 2022, pp 4–11. Published online in International Academic Press (www.IAPress.org) Classification of Aircraft in Remote Sensing Images Based on Deep Convolutional Neural Networks Youssef Ben Youssef 1 , Mohamed Merrouchi 2 , Elhassane Abdelmounime 2 ,Toufiq Gadi 2 1 Hassan first university Settat, Ecole Nationale des Sciences Appliqu´ ees, Berrechid, Morocco 2 Hassan first University Settat, FST of Settat, Morocco Abstract Convolutional Neural Network (CNN) is a component of Deep Learning(DL) recently exploited in different fields. In this work, we improve the performance of multi-label classification based on CNN for remote sensing images of aircraft types. Intensive preprocessing limits the classification rate in previous studies. In order to avoid under-fitting and over-fitting 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 classification rate. Keywords Computer vision, Machine learning, Deep learning, Convolutional Neural Network, Classification AMS 2010 subject classifications 62P30, 68U10 DOI: 10.19139/soic-2310-5070-1143 1. Introduction Image classification is one of the most important field of computer vision and machine learning. Assigning automatically predefined labels to images is that the aim of image classification. One of the important issues in remote sensing image processing is aircraft type classification, and it has been widely used in civil and military applications. To solve this issue, researchers have designed and implemented several methods for the image’s classification. Machine learning(ML) has emerged united of the foremost successful artificial intelligence techniques and has achieved impressive performance within the field of computer vision and image processing, with applications like image medical classification [1][2], remote sensing image scene classification[3], aircraft detection [17] , and aircraft classification [5]. All algorithms in ML are based on many handcrafted features available from images for doing the classification; those methods are named also handcraft descriptors in classification. Recently in remote sensing image classification based on DL is growing. It has been widely applied in diverse areas study, including vegetated areas, urban areas, wetlands, and forest areas[6] . As a result, the details of ground objects, such as contour, structure, and texture information, can be obtained conveniently. Among DL algorithms used in classification, CNN have gained popularity. Since 2012, CNN has attracted more attention because of the increasing computing power, availability of lower-cost hardware, open-source algorithms, and the rise of big data [7] . Getting deeper is an important typical trend of CNN [8] . By increasing depth, CNN can approximate the target function with increased non-linearity and get better descriptor representations. However, the complexity of the network is increasing, which makes the network more difficult to optimize and easier to get under-fitting or over-fitting. The main contributions of this work can be summarized as follows: * Correspondence to: Youssef Ben Youssef (Email: youssef.benyoussef@uhp.ac.ma). Ecole Nationale des Sciences Appliqu´ ees, Avenue de l’universit´ e, B.P :218 Berrechid. Morocco(26100). ISSN 2310-5070 (online) ISSN 2311-004X (print) Copyright © 2022 International Academic Press