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.
Step . | Calculation . |
---|---|
Input | Pre-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 |
Output | An estimated spectral parameter |
Step . | Calculation . |
---|---|
Input | Pre-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 |
Output | An estimated spectral parameter |
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.
Step . | Calculation . |
---|---|
Input | Pre-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 |
Output | An estimated spectral parameter |
Step . | Calculation . |
---|---|
Input | Pre-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 |
Output | An estimated spectral parameter |
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