Hyperspectral remote sensing image classification using three-dimensional-squeeze-and-excitation-DenseNet (3D-SE-DenseNet). Issue 2 (1st February 2020)

• Record Type: Journal Article
• Authors: Li, Guandong; Zhang, Chunju; Lei, Runmin; Zhang, Xueying; Ye, Zhourun; Li, Xiaoli
• Is Part Of: Remote sensing letters. Volume 11:Issue 2(2020)
• Page Start: 195
• Page End: 203
• Publication Date: 2020-02-01
• Subjects: Remote sensing -- Periodicals; Remote sensing; Periodicals; 621.3678
• DOI: 10.1080/2150704X.2019.1697001
• Languages: English
• ISSNs: 2150-704X

Abstract

ABSTRACT: This study introduces the attention mechanism in hyperspectral remote sensing image (HSI) classification which can strengthen the information provided by important features, and weaken the non-essential information. We introduced the Squeeze-and-Excitation (SE) block embedded in three-dimensional densely connected convolutional network (3D-DenseNet) to form 3D-SE-DenseNet for HSI classifications. This model can learn a powerful network with low training costs and fast convergence speed, and avoids overfitting on small sample datasets. Two different 3D-SE-DenseNet models of 3D-SE-DenseNet and 3D-SE-DenseNet-BC were set up. Results from experiments show that the 3D-SE-DenseNet performs well on the Indian Pines, Pavia University, Botswana, and Kennedy Space Centre datasets.