Hyperspectral Image Classification Using Spectral-Spatial Convolutional Neural Networks

Hyperspectral images provide detailed information about the scanned objects, as they capture their spectral characteristics within a large number of wavelength bands. Classification of such data has become an active research topic due to its wide applicability. In this paper, we introduce a new spectral-spatial convolutional neural network, benefitting from a battery of data augmentation techniques which help deal with a real-life problem of lacking ground-truth training data. Our experiments showed that the proposed method works in real time and outperforms other spectral-spatial algorithms.