Segmenting Hyperspectral Images Using Spectral Convolutional Neural Networks in the Presence of Noise

Hyperspectrometers capture detailed information about the scanned objects, but they are susceptible to the presence of various kinds of noise. In this work, we quantify the impact of different levels of Gaussian and impulsive noise on the abilities of a spectral convolutional neural network applied for hyperspectral image segmentation. Our experiments showed that the noise may degrade the classification accuracy of the spectral convolutional nets, and can be a serious obstacle in deploying such models on-board a satellite, where the presence of noise is inevitable.