Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Anna Chlingaryan"'
Autor:
Faysal M. Hasan, Peter C. Thomson, Mohammed R. Islam, Cameron E.F. Clark, Anna Chlingaryan, Sabrina Lomax
Publikováno v:
Smart Agricultural Technology, Vol 9, Iss , Pp 100639- (2024)
Cattle liveweight (LW) monitoring is essential for the effective management of animal productivity and welfare, particularly in decision-making on farms. Traditional static weigh (SW) systems require animals to be moved to fixed scales, posing challe
Externí odkaz:
https://doaj.org/article/bc6a67166a2d4b0b91ee8b002b0f0b67
Publikováno v:
Remote Sensing, Vol 11, Iss 7, p 864 (2019)
This paper proposes novel autoencoders for unsupervised feature-learning from hyperspectral data. Hyperspectral data typically have many dimensions and a significant amount of variability such that many data points are required to represent the distr
Externí odkaz:
https://doaj.org/article/40bf3b395f254cfc988122515a3da14d
Autor:
Adrian Ball, John Zigman, Arman Melkumyan, Anna Chlingaryan, Katherine Silversides, Raymond Leung
Publikováno v:
Springer Proceedings in Earth and Environmental Sciences ISBN: 9783031198441
For banded iron formation-hosted deposits accurate boundary modelling is critical to ore-grade estimation. Key to estimation fidelity is the accurate separation of the different domains within the ore body, requiring modelling of the boundaries betwe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c1ec4c5bc177ca25a4c9b52a8fff16d3
https://doi.org/10.1007/978-3-031-19845-8_17
https://doi.org/10.1007/978-3-031-19845-8_17
Publikováno v:
Geoscience Frontiers. 14:101562
The remote mapping of minerals and discrimination of ore and waste on surfaces are important tasks for geological applications such as those in mining. Such tasks have become possible using ground-based, close-range hyperspectral sensors which can re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::11aad24cb524312e06da0f0d986add5d
https://doi.org/10.1016/j.gsf.2023.101562
https://doi.org/10.1016/j.gsf.2023.101562
Publikováno v:
Expert Systems with Applications. 199:116959
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5e253640a457bdbb3ce33bfc85ace527
https://doi.org/10.1016/j.eswa.2022.116959
https://doi.org/10.1016/j.eswa.2022.116959
This paper presents a Bayesian framework for manipulating mesh surfaces with the aim of improving the positional integrity of the geological boundaries that they seek to represent. The assumption is that these surfaces, created initially using sparse
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab135675265ff71e2422856d1161c160
http://arxiv.org/abs/2005.14427
http://arxiv.org/abs/2005.14427
Publikováno v:
Precision Agriculture. 20:767-787
Variations in water absorption across lettuce leaves (Latuca sativa L. var. longifolia) were quantified from hyperspectral imagery acquired in the laboratory using selected spectral indices, specifically, the Moisture Stress Index (MSI), the Normalis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d2f76b8ebc8671136e6e245034d12a72
https://doi.org/10.1007/s11119-018-9610-5
https://doi.org/10.1007/s11119-018-9610-5
Publikováno v:
Computers and Electronics in Agriculture. 151:61-69
Accurate yield estimation and optimised nitrogen management is essential in agriculture. Remote sensing (RS) systems are being more widely used in building decision support tools for contemporary farming systems to improve yield production and nitrog
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ebb10933dc32a637fae880834e00e5ca
https://doi.org/10.1016/j.compag.2018.05.012
https://doi.org/10.1016/j.compag.2018.05.012
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 56:2798-2810
Convolutional neural networks (CNNs) have been shown to be a powerful tool for image classification. Recently, they have been adopted into the remote sensing community with applications in material classification from hyperspectral images. However, C
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a48e9c281df8cfd2c684d66440858950
https://doi.org/10.1109/tgrs.2017.2783886
https://doi.org/10.1109/tgrs.2017.2783886
Publikováno v:
Remote Sensing, Vol 11, Iss 7, p 864 (2019)
Remote Sensing; Volume 11; Issue 7; Pages: 864
Remote Sensing; Volume 11; Issue 7; Pages: 864
This paper proposes novel autoencoders for unsupervised feature-learning from hyperspectral data. Hyperspectral data typically have many dimensions and a significant amount of variability such that many data points are required to represent the distr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fca8bfcf14522b755aca5ac0232d9069
https://www.mdpi.com/2072-4292/11/7/864
https://www.mdpi.com/2072-4292/11/7/864