Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation
This work addresses the challenge of precise hyperspectral classification for applications in fields like remote sensing, though it appears incremental as it builds on existing deep learning methods.
The paper tackled the problem of hyperspectral image classification with limited ground-truth data by introducing a spectral-spatial convolutional neural network with training-time data augmentation, achieving real-time performance and outperforming other spectral-spatial techniques.
Hyperspectral imaging provides detailed information about the scanned objects, as it captures 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 a variety of fields. Deep learning has established the state of the art in the area, and it constitutes the current research mainstream. In this letter, 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 rigorous experiments showed that the proposed method outperforms other spectral-spatial techniques from the literature, and delivers precise hyperspectral classification in real time.