Open AccessFuSENet: fused squeeze‐and‐excitation network for spectral‐spatial hyperspectral image classification
Author(s) -
Roy Swalpa Kumar,
Dubey Shiv Ram,
Chatterjee Subhrasankar,
Baran Chaudhuri Bidyut
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.1462
Subject(s) - computer science , pooling , artificial intelligence , convolutional neural network , bilinear interpolation , pattern recognition (psychology) , residual , hyperspectral imaging , benchmark (surveying) , deep learning , feature (linguistics) , contextual image classification , network architecture , image (mathematics) , computer vision , algorithm , linguistics , philosophy , computer security , geodesy , geography
Deep learning-based approaches have become very prominent in recent years due to its outstanding performance as compared to the hand-extracted feature-based methods. Convolutional neural network (CNN) is a type of deep learning architecture to deal with the image/video data. Residual network and squeeze and excitation network (SENet) are among recent developments in CNN for image classification. H...