Hyperspectral Data Classification Using Extended Extinction Profiles

This letter proposes a new approach for the spectral–spatial classification of hyperspectral images, which is based on a novel extrema-oriented connected filtering technique, entitled as extended extinction profiles. The proposed approach progressively simplifies the first informative features extracted from hyperspectral data considering different attributes. Then, the classification approach is applied on two well-known hyperspectral data sets, i.e., Pavia University and Indian Pines, and compared with one of the most powerful filtering approaches in the literature, i.e., extended attribute profiles. Results indicate that the proposed approach is able to efficiently extract spatial information for the classification of hyperspectral images automatically and swiftly. In addition, an array-based node-oriented max-tree representation was carried out to efficiently implement the proposed approach

Hyperspectral data classification using extended extinction profiles

This letter proposes a new approach for the spectral-spatial classification of hyperspectral images, which is based on a novel extrema-oriented connected filtering technique, entitled as extended extinction profiles. The proposed approach progressively simplifies the first informative features extracted from hyperspectral data considering different attributes. Then, the classification approach is applied on two well-known hyperspectral data sets, i.e., Pavia University and Indian Pines, and compared with one of the most powerful filtering approaches in the literature, i.e., extended attribute profiles. Results indicate that the proposed approach is able to efficiently extract spatial information for the classification of hyperspectral images automatically and swiftly. In addition, an array-based node-oriented max-tree representation was carried out to efficiently implement the proposed approach131116411645CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP311228/2014-32013/23514-0; 2015/12127-0; 2013/07559-3This work was supported in part by the Alexander von Humboldt Fellowship for Postdoctoral Researchers; by the Helmholtz Young Investigators Group “SiPEO” under Grant VH-NG-1018 by the Fundação de Amparo á Pesquisa do Estado de São Paulo under Grant 2013/23514-0, Grant 2015/12127-0, and Grant 2013/07559-3; and by the CNPq under Grant 311228/2014-