Table 1.

The BGANet network. In step (1), there is a model parameter t, which indicates the number of wavelength subbands; in step (2), there are parameters n and l1, ⋅⋅⋅, ln, which indicate the number of Bi-GRU layers and the dimension of the features of interest of each Bi-GRU learning layer, respectively.

StepCalculation
InputPre-processed spectra
(1)Dividing each spectrum into t subbands
with equal wavelength width
(2)A series of Bi-GRU learning layers
(3)A Self-Attention learning layer
(4)A fully connected learning layer
OutputAn estimated spectral parameter
StepCalculation
InputPre-processed spectra
(1)Dividing each spectrum into t subbands
with equal wavelength width
(2)A series of Bi-GRU learning layers
(3)A Self-Attention learning layer
(4)A fully connected learning layer
OutputAn estimated spectral parameter
Table 1.

The BGANet network. In step (1), there is a model parameter t, which indicates the number of wavelength subbands; in step (2), there are parameters n and l1, ⋅⋅⋅, ln, which indicate the number of Bi-GRU layers and the dimension of the features of interest of each Bi-GRU learning layer, respectively.

StepCalculation
InputPre-processed spectra
(1)Dividing each spectrum into t subbands
with equal wavelength width
(2)A series of Bi-GRU learning layers
(3)A Self-Attention learning layer
(4)A fully connected learning layer
OutputAn estimated spectral parameter
StepCalculation
InputPre-processed spectra
(1)Dividing each spectrum into t subbands
with equal wavelength width
(2)A series of Bi-GRU learning layers
(3)A Self-Attention learning layer
(4)A fully connected learning layer
OutputAn estimated spectral parameter
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