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Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism
2021
Remote Sensing
In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual convolutional neural network model fused with an efficient channel attention network (MRA-NET) that is appropriate for hyperspectral image classification. The suggested technique comprises a multi-staged architecture, where initially the
doi:10.3390/rs13030335
fatcat:ajltujwzmrc4ppf55opardwwf4