Analysis of the influence of 3D-CNN on spatial random information in hyperspectral image classification

Abstract

In the hyperspectral image, the type of material can be known by using the spectral information. Recently, hyperspectral image classification using deep learning has been developed. Among them, 3D-CNN, which learns spatial information and spectral information together, has excellent performance. However, since 3D-CNN learns spatial information and spectral information together, there is a possibility that the spectral information is diluted. This does not correspond to the hyperspectral image in which the spectral information is significant. This paper suggests that 3D-CNN is not doing the right thing to learn about spectral information in hyperspectral image classification. In addition, hyperspectral data with random spatial information is verified through experiments.