# Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification
## 4 Discussion  
The literature on multispectral and hyperspectral image augmentation contains a multitude of different data augmentation techniques, most of which are used in combination with pixel-based classification. There is a notable lack of proposals approaching data augmentation in combination with superpixel-based classification schemes. Examples of the use of geometric transforms in pixel-based classification schemes can be seen in [13; 32; 33]. Attempts to synthesize new samples drawing data from a multivariate normal distribution initialized with the standard deviations of the bands in an HSI can be found in [34]. Reference [15] approaches the generation of synthetic samples to be later used in the training of deep networks through the use of GANs, as does [14]. These generative models are very costly to train and require fine-tuned adjustments of the hyperparameters on a per scene basis. The data augmentation framework proposed in this work leverages the composition of simple transformations that require no parameter tuning in order to achieve robust increases in classification accuracy when used in a superpixel-based classification scheme. This makes the derived methods a convenient replacement for the traditional rotation and flip (mirroring) operations. Reference [35] already introduced the idea of the division of an input patch into several regions in order to better exploit the spatial information. The paper focuses on the selection and extraction of a number of predefined regions surrounding a pixel of interest that are later fed to several CNNs. More examples of the use of the spatial correlation between pixels in a scene can be found in [10], where the authors define pixel-pair features. During the training phase, sample pairs are fed to a CNN architecture that, once trained, is using during the testing phase to compute the labels of the surrounding samples of the pixel being tested and obtain the final label performing a majority vote of the outputs. A similar approach is taken in [11] with pixel-block pairs (PBPs), where the author further builds upon that idea by adding explicit spatial information to the new PBP features. These proposals try to achieve a goal that is similar, in essence, to what DWS does by using the central pixel of each superpixel in order to extract a patch. By selecting the central pixel, the probability of obtaining data from a region with high homogeneity is maximized, yielding an increase in classification accuracy. To the best of our knowledge, no systematic comparison of data augmentation techniques focused on superpixel-based classification has been published as of yet in the field of remote sensing. The existing papers focus on pixel-based classification and the datasets usually comprise small, low-resolution scenes. An additional factor that increases the difficulty when trying to compare the results from different proposals arises from the use of different models or network architectures to obtain them, making the comparison of the quality between different techniques a challenging endeavor. In this work we provide a view of the current data augmentation landscape, by showing a comparison to prove the effectiveness of the proposed DWS approach. The experimental results for all the datasets show that techniques based on the DWS proposal outperform the other techniques from the literature considered in this comparison for the classification of large, high-resolution images. It is important to note that using DWS, training is performed using a single observation per superpixel, reducing the amount of data that has to be evaluated by over two orders of magnitude compared to pixel-based schemes. In most of the previous tests, augmentation techniques making use of the DWS framework showed less dispersion in the results across runs for all scenes, with noticeably smaller standard deviations than traditional techniques.