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Convolutional Neural Networks and Data Augmentation for Spectral-Spatial Classification of Hyperspectral Images

Spectral-spatial classification of remotely sensed hyperspectral images has been the subject of many studies in recent years. Current methods achieve excellent performance on benchmark hyperspectral image labeling tasks when a sufficient number of labeled pixels is available. However, in the presence of only very few labeled pixels, such classification becomes a challenging problem. 
In this paper we propose to tackle this problem using convolutional neural networks (CNNs) and data augmentation. Our newly developed method relies on the assumption of spectral-spatial locality: nearby pixels in a hyperspectral image are related, in the sense that their spectra and their labels are likely to be similar. We exploit this assumption to develop 1) a new data augmentation procedure which adds new samples to the train set and 2) a tailored loss function which penalize differences among weights of the network corresponding to nearby wavelengths of the spectra. We train a simple single layer convolutional neural network with this loss function and augmented train set and use it to classify all unlabeled pixels of the given image. 
To assess the efficacy of our method, we used five publicly available hyperspectral images: Pavia Center, Pavia University, KSC, Indian Pines and Salina. On these images our method significantly outperforms other baselines. Notably, with just 1% of labeled pixels per class, on these dataset our method achieves an accuracy of 99.5%, etc. Furthermore we show that our method improves over other baselines also in a supervised setting, when no overlap between train and test pixels is allowed. 
Overall our investigation demonstrates that spectral-spatial locality can be easily embedded in a simple convolutional neural network through data augmentation and a tailored loss function.