Zobrazeno 1 - 10
of 126
pro vyhledávání: '"HAMMOUCH, Ahmed"'
Publikováno v:
International Journal of Signal and Imaging Systems Engineering, 2018, 11(4), pp. 193-205. - http://www.scopus.com/inward/record.url?eid=2-s2.0-85051431092&partnerID=MN8TOARS
Dimensionality reduction is an important preprocessing step of the hyperspectral images classification (HSI), it is inevitable task. Some methods use feature selection or extraction algorithms based on spectral and spatial information. In this paper,
Externí odkaz:
http://arxiv.org/abs/2211.00446
Publikováno v:
Proceedings - 3rd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2017, 2017, 8075549 - http://www.scopus.com/inward/record.url?eid=2-s2.0-85035329769&partnerID=MN8TOARS
The high dimensionality of hyperspectral images often imposes a heavy computational burden for image processing. Therefore, dimensionality reduction is often an essential step in order to remove the irrelevant, noisy and redundant bands. And conseque
Externí odkaz:
http://arxiv.org/abs/2210.16496
Publikováno v:
Procedia Computer Science, 2019, 148, pp. 97-106 - http://www.scopus.com/inward/record.url?eid=2-s2.0-85048829863&partnerID=MN8TOARS
Nowadays, the hyperspectral remote sensing imagery HSI becomes an important tool to observe the Earth's surface, detect the climatic changes and many other applications. The classification of HSI is one of the most challenging tasks due to the large
Externí odkaz:
http://arxiv.org/abs/2210.15422
Over the past decades, the hyperspectral remote sensing technology development has attracted growing interest among scientists in various domains. The rich and detailed spectral information provided by the hyperspectral sensors has improved the monit
Externí odkaz:
http://arxiv.org/abs/2210.15546
Publikováno v:
International Conference Europe Middle East & North Africa Information Systems and Technologies to Support Learning. Springer, Cham, 2018. p. 521-530
Band selection is a great challenging task in the classification of hyperspectral remotely sensed images HSI. This is resulting from its high spectral resolution, the many class outputs and the limited number of training samples. For this purpose, th
Externí odkaz:
http://arxiv.org/abs/2210.15477
Publikováno v:
Procedia Computer Science, 2019, 148, pp. 126-134. DOI: 10.1016/j.procs.2019.01.016 - http://www.scopus.com/inward/record.url?eid=2-s2.0-85062681587&partnerID=MN8TOARS
Recently, the hyperspectral sensors have improved our ability to monitor the earth surface with high spectral resolution. However, the high dimensionality of spectral data brings challenges for the image processing. Consequently, the dimensionality r
Externí odkaz:
http://arxiv.org/abs/2210.15027
Publikováno v:
2016 International Conference on Electrical and Information Technologies (ICEIT) - http://www.scopus.com/inward/record.url?eid=2-s2.0-84992221810&partnerID=MN8TOARS
The high dimensionality of hyperspectral images (HSI) that contains more than hundred bands (images) for the same region called Ground Truth Map, often imposes a heavy computational burden for image processing and complicates the learning process. In
Externí odkaz:
http://arxiv.org/abs/2210.14609
Publikováno v:
2014 2nd World Conference on Complex Systems, WCCS 2014, 2015, pp. 659-664, 7060990. http://www.scopus.com/inward/record.url?eid=2-s2.0-84929207477&partnerID=MN8TOARS
The Remote sensing provides a synoptic view of land by detecting the energy reflected from Earth's surface. The Hyperspectral images (HSI) use perfect sensors that extract more than a hundred of images, with more detailed information than using tradi
Externí odkaz:
http://arxiv.org/abs/2210.16237
Publikováno v:
2015 Intelligent Systems and Computer Vision, ISCV 2015, 2015, 7106167 - http://www.scopus.com/inward/record.url?eid=2-s2.0-84934343941&partnerID=MN8TOARS
The Hyperspectral image (HSI) contains several hundred bands of the same region called the Ground Truth (GT). The bands are taken in juxtaposed frequencies, but some of them are noisily measured or contain no information. For the classification, the
Externí odkaz:
http://arxiv.org/abs/2210.16239
Publikováno v:
Proceedings of the 2018 International Conference on Optimization and Applications, ICOA 2018, 2018, pp. 1-7 http://www.scopus.com/inward/record.url?eid=2-s2.0-85048829863&partnerID=MN8TOARS
Feature selection is one of the most important problems in hyperspectral images classification. It consists to choose the most informative bands from the entire set of input datasets and discard the noisy, redundant and irrelevant ones. In this conte
Externí odkaz:
http://arxiv.org/abs/2210.14346