A lightweight depthwise separable dilated convolutional network with an MSE, spectral-angle, and L2 loss achieves competitive hyperspectral super-resolution on PaviaC/PaviaU with 0.96M parameters.
Supervised nonlinear spectral unmixing using a postnonlinear mixing model for hyperspectral imagery,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.IV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Towards Lightweight Hyperspectral Image Super-Resolution with Depthwise Separable Dilated Convolutional Network
A lightweight depthwise separable dilated convolutional network with an MSE, spectral-angle, and L2 loss achieves competitive hyperspectral super-resolution on PaviaC/PaviaU with 0.96M parameters.