A Multiscale Deep Learning Approach for High-Resolution Hyperspectral Image Classification

Hyperspectral imagery (HSI) has emerged as a highly successful sensing modality for a variety of applications ranging from urban mapping to environmental monitoring and precision agriculture. Despite the efforts made by the scientific community, developing reliable algorithms of HSI classification remains a challenging problem, especially for high-resolution HSI data where there is often larger intraclass variability combined with a scarcity of the ground-truth data and class imbalance. In recent years, deep neural networks have emerged as a promising strategy for problems of HSI classification where they have shown remarkable potential for learning joint spectral–spatial features efficiently via backpropagation. In this letter, we propose a deep learning strategy for HSI classification that combines different convolutional neural networks, especially designed to efficiently learn joint spatial–spectral features over multiple scales. Our method achieves an overall classification accuracy of 66.73% on the 2018 IEEE GRSS hyperspectral data set—a high-resolution data set that includes 20 urban land-cover and land-use classes.