Hyperspectral remote sensing image classification using three-dimensional-squeeze-and-excitation-DenseNet (3D-SE-DenseNet)

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.

Additional information

Funding

This work was jointly supported by the National Natural Science Foundation of China (Grant No.41671456), foundation of Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education (Grant No.GTYR201907) and the National Key Research and Development Program of China (Grant No.2017YFB0503602).