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Ensemble learning for hyperspectral image classification using tangent collaborative representation / Hongjun Su in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) . - pp 3778 - 3790 Titre : Ensemble learning for hyperspectral image classification using tangent collaborative representation Type de document : Article/Communication Auteurs : Hongjun Su, Auteur ; Yao Yu, Auteur ; Qian Du, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3778 - 3790 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image numérique [Termes IGN] boosting adapté [Termes IGN] Bootstrap (statistique) [Termes IGN] classification [Termes IGN] classification dirigée [Termes IGN] classification orientée objet [Termes IGN] conception collaborative [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] échantillon [Termes IGN] image hyperspectrale [Termes IGN] neurone artificiel [Termes IGN] performance [Termes IGN] régression Résumé : (auteur) Recently, collaborative representation classification (CRC) has attracted much attention for hyperspectral image analysis. In particular, tangent space CRC (TCRC) has achieved excellent performance for hyperspectral image classification in a simplified tangent space. In this article, novel Bagging-based TCRC (TCRC-bagging) and Boosting-based TCRC (TCRC-boosting) methods are proposed. The main idea of TCRC-bagging is to generate diverse TCRC classification results using the bootstrap sample method, which can enhance the accuracy and diversity of a single classifier simultaneously. For TCRC-boosting, it can provide the most informative training samples by changing their distributions dynamically for each base TCRC learner. The effectiveness of the proposed methods is validated using three real hyperspectral data sets. The experimental results show that both TCRC-bagging and TCRC-boosting outperform their single classifier counterpart. In particular, the TCRC-boosting provides superior performance compared with the TCRC-bagging. Numéro de notice : A2020-280 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2957135 Date de publication en ligne : 01/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2957135 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95100 [article]

Local binary patterns and extreme learning machine for hyperspectral imagery classification / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 7 (July 2015) . - pp 3681 - 3693 Titre : Local binary patterns and extreme learning machine for hyperspectral imagery classification Type de document : Article/Communication Auteurs : Wei Li, Auteur ; Chen Chen, Auteur ; Hongjun Su, Auteur ; Qian Du, Auteur Année de publication : 2015 Article en page(s) : pp 3681 - 3693 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] classification spectrale [Termes IGN] extraction de traits caractéristiques [Termes IGN] filtre de Gabor [Termes IGN] image hyperspectrale [Termes IGN] texture d'image Résumé : (Auteur) It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and spots. Two levels of fusion (i.e., feature-level fusion and decision-level fusion) are applied to the extracted LBP features along with global Gabor features and original spectral features, where feature-level fusion involves concatenation of multiple features before the pattern classification process while decision-level fusion performs on probability outputs of each individual classification pipeline and soft-decision fusion rule is adopted to merge results from the classifier ensemble. Moreover, the efficient extreme learning machine with a very simple structure is employed as the classifier. Experimental results on several HSI data sets demonstrate that the proposed framework is superior to some traditional alternatives. Numéro de notice : A2015-316 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2381602 En ligne : https://doi.org/10.1109/TGRS.2014.2381602 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76566 [article]

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