A new data augmentation technique for the CNN-based classification of hyperspectral imagery
Deep Learning (DL)-based classification schemes for hyperspectral remotely sensed data have been introduced in the last few years with remarkable success due to their capability to learn the non-linear nature of the information that conforms hyperspectral images. In particular, Convolutional Neural Networks (CNNs) have been successfully used for solving problems requiring multi-class classification in the remote sensing field involving feature extraction. CNNs operate over small cubes of the dataset called patches centered in the pixels of the image instead of relying only on the spectral information corresponding to each pixel. These networks require a high number of observations to properly produce a generalized model. In these circumstances data augmentation techniques can help alleviate the problem by generating new, synthetic samples from existing data. Imputation is a statistical technique consisting in filling or replacing missing observations or values of a subset of observations by others obtained via inference from the original dataset. In this paper, a preliminary idea for a data augmentation technique based on the use of data imputation techniques for CNN classification is presented. Different hyperspectral images of the Earth surface widely used in the remote sensing field have been considered as test datasets. The results show the viability of the preliminary idea.
keywords: Hyperspectral, Data augmentation, CNN, Data imputation