A CNN Ensemble Based on a Spectral Feature Refining Module for Hyperspectral Image Classification

Abstract

In the study of hyperspectral image classification based on machine learning theory and techniques, the problems related to the high dimensionality of the images and the scarcity of training samples are widely discussed as two main issues that limit the performance of the data-driven classifiers. These two issues are closely interrelated, but are usually addressed separately. In our study, we try to kill two birds with one stone by constructing an ensemble of lightweight base models embedded with spectral feature refining modules. The spectral feature refining module is a technique based on the mechanism of channel attention. This technique can not only perform dimensionality reduction, but also provide diversity within the ensemble. The proposed ensemble can provide state-of-the-art performance when the training samples are quite limited. Specifically, using only a total of 200 samples from each of the four popular benchmark data sets (Indian Pines, Salinas, Pavia University and Kennedy Space Center), we achieved overall accuracies of 89.34%, 95.75%, 93.58%, and 98.14%, respectively.