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Hyperspectral imaging provides detailed information about the scannedobjects, as it captures their spectral characteristics within a large number ofwavelength bands. Classification of such data has become an active researchtopic due to its wide applicability in a variety of fields. Deep learning hasestablished the state of the art in the area, and it constitutes the currentresearch mainstream. In this letter, we introduce a new spectral-spatialconvolutional neural network, benefitting from a battery of data augmentationtechniques which help deal with a real-life problem of lacking ground-truthtraining data. Our rigorous experiments showed that the proposed methodoutperforms other spectral-spatial techniques from the literature, and deliversprecise hyperspectral classification in real time.

Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation