Hyperspectral imagery classification with cascaded support vector machines and multi-scale superpixel segmentation

Hyperspectral imagery (HSI) classification is a rapidly growing and highly active research area in the field of hyperspectral community. The method that combines both spatial and spectral information for hyperspectral image classification has made a great advance. The focus of spectral-spatial classification is how to extract discriminating features and how to combine the spectral and spatial information effectively. In this paper, we propose a new spectral-spatial method to solve these two problems. The first part is a series of support vector machines (SVMs) that are cascaded to form the enhanced features, where the predicted information of the preceding layer can provide correction information for the subsequent layer, and then a new superpixel segmentation method is adopted to introduce multi-scale spatial information, which can generate multi-scale superpixels at one time and is as accurate as the state-of-the-art methods. The final label is determined by majority voting of different scales.