| 2/2024 - 8 |
Development of a Very Low-Cost Deforestation Monitoring System Based on Aerial Image Clustering and Compression TechniquesANDREI, A.-T.   , GRIGORE, O. |
Extra paper information in    |
| Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science |
Download PDF  (3,579 KB) | Citation | Downloads: 415 | Views: 437 |
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. |
| References | | | Cited By «-- Click to see who has cited this paper |
| [1] K. Pearson, "Contributions to the mathematical theory of evolution," University College, London, vol. 185, pp. 71-110, 1894. [CrossRef] [2] A. P. Dempster, N. M. Laird, D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," Royal Statistical Society, vol. 39, no. 1, pp. 1-22, 1977. [CrossRef] [Web of Science Times Cited 33361] [3] Y. Tarabalka, J. A. Benediktsson, J. Chanussot, "Spectral-spatial classification of hyperspectral imagery based on partitional clustering techniques," IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 8, pp. 2973-2987, 2009. [CrossRef] [Web of Science Times Cited 566] [SCOPUS Times Cited 661] [4] H. Permuter, J. Francos, I. Jermyn, "A study of Gaussian mixture models of color and texture features for image classification and segmentation," Pattern Recognition, Elsevier, vol. 39, no. 4, pp. 695-706, 2006. [CrossRef] [Web of Science Times Cited 247] [SCOPUS Times Cited 325] [5] X.-H. Wu, R. Hu, Y.-Q. Bao, "Coarse-to-fine method for vision-based pedestrian traffic light detection," Advances in Electrical and Computer Engineering, vol. 20, no. 1, pp. 43-48, 2020. [CrossRef] [Full Text] [Web of Science Times Cited 1] [SCOPUS Times Cited 1] [6] A.-T., Andrei, O. Grigore, "Unsupervised machine learning algorithms used in deforested areas monitoring," International Conference on E-Health and Bioengineering, Iasi, Romania, pp. 1-4, 2021. [CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 7] [7] A.-T., Andrei, O. Grigore, "Combating deforestation using different AGNES approaches," 14th International Conference on Communications (COMM), Bucharest, Romania, pp. 1-5, 2022. [CrossRef] [SCOPUS Times Cited 4] [8] A.-T., Andrei, O. Grigore, "Mean shift clustering with bandwidth estimation and color extraction module used in forest segmentation," 13th International Symposium on Advanced Topics in Electrical Engineering (ATEE), Bucharest, Romania, pp. 1-6, 2023. [CrossRef] [SCOPUS Times Cited 2] [9] A.-T., Andrei, O. Grigore, "Gaussian mixture model application in deforestation monitoring," International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey, pp. 26-31, 2022. [CrossRef] [SCOPUS Times Cited 4] [10] W. Emery, A. Camps, "Chapter 3 - Optical imaging systems, introduction to satellite remote sensing," Introduction to Satellite Remote Sensing, Elsevier, pp. 85-130, 2017. [CrossRef] [Web of Science Times Cited 4] [11] H. Al-Jubouri, H. Du, H. Sellahewa, "Applying Gaussian mixture model on discrete cosine features for image segmentation and classification," 4th Computer Science and Electronic Engineering Conference (CEEC), Colchester, UK, pp. 194-199, 2012. [CrossRef] [SCOPUS Times Cited 7] [12] L. Jiao, T. Denoeux, Z. Liu, Q. Pan, "EGMM: An evidential version of the Gaussian mixture model for clustering," Applied Soft Computing, vol. 129, no. C, 2022. [CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 28] [13] N. X. Vinh, J. Epps, J. Bailey, "Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance," The Journal of Machine Learning Research, vol. 11, pp. 2837-2854, 2010. [14] W. M. Rand, "Objective criteria for the evaluation of clustering methods," Journal of the American Statistical Association, pp. 846-850, 2012. [CrossRef] [SCOPUS Times Cited 4787] [15] G. Exarchakis, O. Oubari, G. Lenz, "A sampling-based approach for efficient clustering in large datasets," 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12393-12402, 2022. [CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 4] [16] K. R. Rao, P. Yip, "Discrete cosine transform," Algorithms, Advantage Applications, Academic Press, 1990. [CrossRef] [17] R. Gonzalez, R. Woods, "Digital image processing," Addison-Wesley Publishing Company, pp. 81-125, 1992 [18] C. K. Chui, "An introduction to wavelets," Mathematics of Computation, JSTOR, vol. 60, no. 202, pp. 854, 1993. [CrossRef] [19] D. A. Reynolds, "Gaussian mixture models," Encyclopedia of biometrics, Springer, Boston, MA, pp. 659-663, 2009. [CrossRef] [20] S. K. Ng, T. Krishnan, G. J. McLachlan, "The EM algorithm," Handbook of Computational Statistics, Springer, Berlin, Heidelberg, pp. 139-172, 2012. [CrossRef] [21] T. O. Hodson, "Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not," Geoscientific Model Development, vol. 15, no. 14, pp. 5481-5487, 2022. [CrossRef] [Web of Science Times Cited 357] [SCOPUS Times Cited 450] [22] D. L. Davies, D. W. Bouldin, "A cluster separation measure," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-1, no. 2, pp. 224-227, 1979. [CrossRef] [23] G. Hudson, A. Leger, B. Niss, I. Sebestyen, J. Vaaben, "JPEG-1 standard 25 years: Past, present, and future reasons for a success," Journal of Electronic Imaging, vol. 27, no. 4, 2018. [CrossRef] [Web of Science Times Cited 42] [SCOPUS Times Cited 55] [24] T. Giannakopoulos, A. Pikrakis, "Signal transforms and filtering essentials," Academic Press, pp. 33-57, 2014. [CrossRef] [25] W. B. Pennebaker, L. J. Mitchell, "JPEG: Still image data compression standard," Van Nostrand Reinhold, 1993 [26] C. M. Bishop, "Pattern recognition and machine learning," Springer Science, pp. 424-430, 2006. [CrossRef] [27] F. Cao, L. Liu, L. Li, "Short-wave infrared photodetector," Materials Today, vol. 62, pp. 327-349, 2023. [CrossRef] [Web of Science Times Cited 65] [SCOPUS Times Cited 71] [28] T. Zhang, T. Zeng, X. Zhang, "Synthetic aperture radar (SAR) meets deep learning," Remote Sensing, vol. 15, no. 2, pp. 303, 2023. [CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 16] [29] T. N. Tu, H. N. Hoang, D. D. Van, T. V. Van, A. D. Duy, "An improvement of kmeans algorithm using wavelet technique to increase speed of clustering remote sensing images", International Journal of Computer and Electrical Engineering, vol. 8, no. 2, pp.177-184, 2016. [CrossRef] [30] X. Zou, X. Xu, C. Qing, X. Xing, "High speed deep networks based on Discrete Cosine Transformation," 2014 IEEE International Conference on Image Processing (ICIP), Paris, France, pp. 5921-5925, 2014. [CrossRef] [SCOPUS Times Cited 21] [31] A.-T., Andrei, O. Grigore, "Low-cost optimized u-net model with gmm automatic labeling used in forest semantic segmentation", Sensors, vol. 23, no. 21, pp. 8991, 2023. [CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 1] Web of Science® Citations for all references: 34,689 TCR SCOPUS® Citations for all references: 6,444 TCR Web of Science® Average Citations per reference: 1,084 ACR SCOPUS® Average Citations per reference: 201 ACR TCR = Total Citations for References / ACR = Average Citations per Reference We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more Citations for references updated on 2024-11-17 18:49 in 188 seconds. Note1: Web of Science® is a registered trademark of Clarivate Analytics. Note2: SCOPUS® is a registered trademark of Elsevier B.V. Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site. |
Copyright ©2001-2024
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.
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.
