Meta-scalable discriminate analytics for Big hyperspectral data and applications
作者：
Highlights：
• Proposed computation framework for Big hyperspectral data discriminate analytics.
• Identified Big data challenges in remote sensing applications.
• Employed divide-conquer scalability for implementation on parallel architectures.
• Discussed transforming the divide-conquer mechanism to be meta-scalable.
• Showed discriminate analytics with proposed mechanism can give optimal solution.
摘要
•Proposed computation framework for Big hyperspectral data discriminate analytics.•Identified Big data challenges in remote sensing applications.•Employed divide-conquer scalability for implementation on parallel architectures.•Discussed transforming the divide-conquer mechanism to be meta-scalable.•Showed discriminate analytics with proposed mechanism can give optimal solution.
论文关键词：Big data,Hyperspectral data,Discriminate analytics,Scalability,Parallel computing and architecture
论文评审过程：Received 7 June 2020, Revised 20 February 2021, Accepted 20 February 2021, Available online 6 March 2021, Version of Record 4 April 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.114777
