# Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification
## 1 Introduction  
Hyperspectral images (HSIs) are formed by a grid of pixels, each of them represented by a high-dimensional vector capturing a fraction of the electromagnetic spectrum for that point, sampled at different wavelengths [1]. The high density of the spectral information contained in HSIs, in the order of tens to hundreds of bands for a single scene, increases the ability to identify the materials present in it. This characteristic makes HSIs popular candidates for supervised classification in the field of remote sensing [2]. Deep learning (DL) models have been introduced in the last few years for HSI classification tasks [3; 4; 5; 6] with promising results. Convolutional neural networks (CNNs) [7] in particular, have been successfully used for solving problems requiring multi-class, multi-label classification involving feature extraction (FE) from images [8]. CNNs operate over small cubes of data called patches instead of relying on spectral information alone. These patches are centered around a pixel of the image andtaken from a sliding window of a certain size in order to extract spatial-spectral information. A patch is extracted for every pixel of the image using this procedure. CNNs generally require large amounts of training samples in order to prevent overfitting. Data augmentation is a technique that synthetically generates new samples by applying a set of domain-specific transformations over the original input dataset to improve the generalization capabilities of a classification model. Several data augmentation techniques applicable to HSIs have been proposed, most of which are based on geometric transformations commonly used for image recognition tasks [9]. More recently, [10; 11] described hyperspectral data augmentation techniques where pixels are grouped in blocks and different block pairs are used as the input to a CNN. In [12], samples in the original dataset were shifted along its first principal component or based on the average value in each band. Augmentation based on randomly erasing parts of the input patches has also been proven effective for HSI classification in [13]. Finally, generative adversarial networks have been proposed recently as a data augmentation technique in order to generate new samples mimicking the distribution of the original data [14; 15; 16]. Segmentation is a preprocessing technique capable of simplifying images, reducing them to meaningful, independent regions of pixels with high intra-region similarity and high inter-region dissimilarity called segments. The simplification makes this technique useful to reduce the complexity of subsequent processing tasks. Examples of the use of superpixels in hyperspectral classification as a way to exploit context information can be found in [17; 18; 19], and as part of a DL classification scheme in [20; 21]. In our proposal, superpixel segmentation [22] is used to reduce the computational cost associated with CNN-based classification. During the training and prediction stages, only a representative subset of pixels from each superpixel of the image is selected, allowing for a significant reduction of the computational cost when compared to the sliding window extraction from pixel-based classification. In this paper, several augmentation techniques relying on geometric transformations aimed at efficient, superpixel-based DL classification for large images are introduced. The main contributions are:  
1. A data augmentation framework called dual-window superpixel (DWS), based on a combination of superpixel segmentation for patch extraction and geometric transformations is proposed. Patches are divided into two regions and the transformations are applied independently to them. This framework is introduced as part of a CNN classification scheme capable of improving the classification accuracy and significantly reducing the execution time of the classification process. 2. A number of fast and simple data augmentation techniques based on the DWS data augmentation framework are also proposed.