Machine Learning with Remote Sensing Image Data Sets

Petrovska, Biserka and Atanasova-Pacemska, Tatjana and Stojkovic, Natasa and Stojanova, Aleksandra and Kocaleva, Mirjana (2021) Machine Learning with Remote Sensing Image Data Sets. Informatica, 45 (3). pp. 347-358. ISSN 0350-5596

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Abstract

Computer vision, as a part of machine learning, gains significant attention from researches nowadays. Aerial scene classification is a prominent chapter of computer vision with a vast application: military, surveillance and security, environment monitoring, detection of geospatial objects, etc. There are several publicly available remote sensing image datasets, which enable the deployment of various aerial scene classification algorithms. In our article, we use transfer learning from pre-trained deep Convolutional Neural Networks (CNN) within remote sensing image classification. Neural networks utilized in our research are high-dimensional previously trained CNN on ImageNet dataset. Transfer learning can be performed through feature extraction or fine-tuning. We proposed a two-stream feature extraction method and afterward image classification through a handcrafted classifier. Fine-tuning was performed with adaptive learning rates and a regularization method label smoothing. The proposed transfer learning techniques were validated on two remote sensing image datasets: WHU RS datasets and AID dataset. Our proposed method obtained competitive results compared to state-of-the-art methods.

Item Type:	Article
Subjects:	Natural sciences > Computer and information sciences
Divisions:	Faculty of Computer Science
Depositing User:	Mirjana Kocaleva Vitanova
Date Deposited:	15 Nov 2021 10:33
Last Modified:	15 Nov 2021 10:33
URI:	https://eprints.ugd.edu.mk/id/eprint/28749

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