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- W4308390392 abstract "By using the cloud computing platform that has achieved excellent commercial results to perform parallel classification processing of massive remote sensing data, it can meet the requirements of improving the parallel processing efficiency of massive remote sensing data. The purpose of this paper is to study the application of distributed cloud computing architecture in hyperspectral remote sensing image classification based on big data. The related technologies of cloud computing are expounded, and the model of SCSRC, a sparse representation classification method based on spatial correlation constraints, is proposed. By analyzing the principle and solution method of SCSRC model, a distributed classification algorithm DP-SCSRC based on spatial correlation constraint sparse representation is proposed on the Spark platform, and the following optimization methods are designed for the core steps. Finally, through experiments with different computing nodes and different amounts of computing data, it is proved that the DP-SCSRC algorithm has a good distributed speedup ratio and data scalability under the premise of ensuring the classification accuracy." @default.
- W4308390392 created "2022-11-11" @default.
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- W4308390392 date "2022-09-23" @default.
- W4308390392 modified "2023-09-26" @default.
- W4308390392 title "Distributed Cloud Computing Architecture in Hyperspectral Remote Sensing Image Classification under Big Data" @default.
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- W4308390392 doi "https://doi.org/10.1109/iciscae55891.2022.9927624" @default.
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