Fast and Accurate Terrain Image Classification for ASTER Remote Sensing by Data Stream Mining and Evolutionary-EAC Instance-Learning-Based Algorithm

Remote sensing streams continuous data feed from the satellite to ground station for data analysis. Often the data analytics involves analyzing data in real-time, such as emergency control, surveillance of military operations or scenarios that change rapidly. Traditional data mining requires all the...

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Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 13; číslo 6; s. 1123
Hlavní autoři:	Hu, Shimin, Fong, Simon, Yang, Lili, Yang, Shuang-Hua, Dey, Nilanjan, Millham, Richard C., Fiaidhi, Jinan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 16.03.2021
Témata:	Accuracy Algorithms Approximation Approximation method ASTER Classification Data analysis Data mining Data processing data stream mining Data transmission Emergency procedures Evolution Evolutionary algorithms evolutionary computing feature selection Ground stations image analysis Image classification landscapes Latency Learning algorithms Machine learning Military operations monitoring Multivariate analysis Object recognition Optimization Pattern recognition Remote sensing Satellites Subgroups Subspaces Surveillance
ISSN:	2072-4292, 2072-4292
On-line přístup:	Získat plný text
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