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Type: Artigo
Title: Hyperspectral Data Classification Using Extended Extinction Profiles
Author: Ghamisi
Pedram; Souza
Roberto; Benediktsson
Jon Atli; Rittner
Leticia; Lotufo
Roberto; Zhu
Xiao Xiang
Abstract: 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.
Subject: Extended Multiextinction Profile (emep)
Hyper-spectral Data Classification
Random Forests (rfs)
Support Vector Machines (svms)
Editor: IEEE-Inst Electrical Electronics Engineers Inc
Rights: fechado
Identifier DOI: 10.1109/LGRS.2016.2600244
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

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