The prevailing framework consisted of complex feature extractors following by conventional classifiers. Nevertheless, the high spatial and high spectral dimensionality of each pixel in the hyperspectral imagery hinders the development of hyperspectral image classification. Fortunately, since 2012, deep learning models, which can extract the hierarchical features of large amounts of daily three-channel optical images, have emerged as a better alternative to their shallow learning counterparts. Within all deep learning models, convolutional neural networks (CNNs) exhibit convincing and stunning ability to process a huge mass of data. In this paper, the CNNs have been adopted as an end-to-end pixelwise scheme to classify the pixels of hyperspectral imagery, in which each pixel contains hundreds of continuous spectral bands. According to the preliminarily qualitative and quantitative results, the existing CNN models achieve promising classification accuracy and process effectively and robustly on the University of Pavia dataset.
Classification of hyperspectral imagery with neural networks: comparison to conventional tools
