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Development of a Very Low-Cost Deforestation Monitoring System Based on Aerial Image Clustering and Compression TechniquesANDREI, A.-T.   , GRIGORE, O. |
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Author keywords
computer vision, discrete cosine transforms, discrete Fourier transforms, discrete wavelet transforms, Gaussian mixture model
References keywords
clustering(6), sensing(5), remote(5), model(5), mixture(5), image(5), grigore(5), gaussian(5), segmentation(4), pattern(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2024-05-31
Volume 24, Issue 2, Year 2024, On page(s): 73 - 84
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2024.02008
Web of Science Accession Number: 001242091800008
SCOPUS ID: 85195651048
Abstract
Clustering holds significant utility across a spectrum of several domains, including pattern recognition, image analysis, customer analytics, market segmentation, social network analysis, and numerous other areas. The main advantages of image clustering are its degree of freedom regarding data labeling and the lack of training and model deployment, which makes them suitable for the overall studys purpose of land cover segmentation and deforestation monitoring. In previous work, the Gaussian Mixture Model (GMM) technique has been established as the best option. Due to the necessity of implementing the algorithm on light unmanned airborne platforms for fast deforestation monitoring, the high resources and long computation time became an issue. This paper proposes several cost-efficient GMM clustering algorithms based on discrete transforms traditionally used for image compression. The results will show that the proposed methods maintain the clustering output quality while drastically decreasing the computation time and also lowering the memory needed to perform. |
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
