A Semantic Segmentation Framework for Hyperspectral Imagery Based on Tucker Decomposition and 3DCNN Tested with Simulated Noisy Scenarios

Article

Abstract: The present work, unlike others, does not try to reduce the noise in hyperspectral images to increas

Palabras claves:

3D convolutional neural network, noisy hyperspectral image, Semantic Segmentation, spectral–spatial feature extraction, Tucker tensor decomposition

Autores:

Andrés Mendez-Vazquez, Padilla-Zepeda E., Torres-Roman D.

Fuentes:

scopus