Spatial–Spectral Squeeze-and-Excitation Residual Network for Hyperspectral Image Classification

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 7; p. 884
• Main Authors: Wang, Li, Peng, Jiangtao, Sun, Weiwei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2019
• Subjects: Band spectra; Banded structure; Classification; convolutional neural networks; Deep learning; Earth science; Excitation spectra; hyperspectral images; Hyperspectral imaging; Image classification; International conferences; Machine learning; Modules; Neighborhoods; Neural networks; Pattern recognition; Pixels; Remote sensing; Spatial data; Spectral bands; spectral–spatial feature extraction; squeeze and excitation; Beijing China; United States > US; China

Jointly using spectral and spatial information has become a mainstream strategy in the field of hyperspectral image (HSI) processing, especially for classification. However, due to the existence of noisy or correlated spectral bands in the spectral domain and inhomogeneous pixels in the spatial neig...