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Auteur Qian Du |
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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)
[article]
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] régressionRésumé : (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
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) . - pp 3778 - 3790[article]Change detection based on stacked generalization system with segmentation constraint / Kun Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 11 (November 2018)
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Titre : Change detection based on stacked generalization system with segmentation constraint Type de document : Article/Communication Auteurs : Kun Tan, Auteur ; Yusha Zhang, Auteur ; Qian Du, Auteur ; Peijun Du, Auteur ; Xiao Jin, Auteur ; Jiayi Li, Auteur Année de publication : 2018 Article en page(s) : pp 733 - 741 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] détection de changement
[Termes IGN] image Quickbird
[Termes IGN] image ZiYuan-3
[Termes IGN] segmentation d'imageRésumé : (Auteur) Change detection based on a multi-classifier ensemble system can take advantage of multiple classifiers to extract change information in remote sensing images. In this paper, an efficient heterogeneous ensemble algorithm, i.e., the stacked generalization (SG) combined with image segmentation, is proposed to construct a simple multi-classifier ensemble system that can offer better detection accuracy with lower computational cost. Due to the rich spatial information in high-spatial-resolution remote sensing images, structure texture (morphological) and statistical texture features are extracted to construct the input data to the ensemble system along with spectral features. In addition, constrained analysis on segmented objects integrates the smaller heterogeneity segmentation map and pixel-wise change map to generate the final change map. The experiments were carried out on two ZY-3 and a QuickBird dataset. The results show that the proposed algorithm can integrate the advantages of both pixel-wise ensemble and object-oriented methods, and effectively improve the accuracy and stability of change detection. Numéro de notice : A2018-485 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.11.733 Date de publication en ligne : 01/11/2018 En ligne : https://doi.org/10.14358/PERS.84.11.733 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91210
in Photogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 11 (November 2018) . - pp 733 - 741[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2018111 RAB Revue Centre de documentation En réserve L003 Disponible Multisource remote sensing data classification based on convolutional neural network / Xiaodong Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)
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Titre : Multisource remote sensing data classification based on convolutional neural network Type de document : Article/Communication Auteurs : Xiaodong Xu, Auteur ; Wei Li, Auteur ; Qiong Ran, Auteur ; Qian Du, Auteur ; Lianru Gao, Auteur ; Bing Zhang, Auteur Année de publication : 2018 Article en page(s) : pp 937 - 949 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction automatique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] réseau neuronal convolutifRésumé : (Auteur) As a list of remotely sensed data sources is available, how to efficiently exploit useful information from multisource data for better Earth observation becomes an interesting but challenging problem. In this paper, the classification fusion of hyperspectral imagery (HSI) and data from other multiple sensors, such as light detection and ranging (LiDAR) data, is investigated with the state-of-the-art deep learning, named the two-branch convolution neural network (CNN). More specific, a two-tunnel CNN framework is first developed to extract spectral-spatial features from HSI; besides, the CNN with cascade block is designed for feature extraction from LiDAR or high-resolution visual image. In the feature fusion stage, the spatial and spectral features of HSI are first integrated in a dual-tunnel branch, and then combined with other data features extracted from a cascade network. Experimental results based on several multisource data demonstrate the proposed two-branch CNN that can achieve more excellent classification performance than some existing methods. Numéro de notice : A2018-191 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2756851 Date de publication en ligne : 16/10/2017 En ligne : https://doi.org/10.1109/TGRS.2017.2756851 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89856
in IEEE Transactions on geoscience and remote sensing > vol 56 n° 2 (February 2018) . - pp 937 - 949[article]Sparse and low-rank graph for discriminant analysis of hyperspectral imagery / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
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Titre : Sparse and low-rank graph for discriminant analysis of hyperspectral imagery Type de document : Article/Communication Auteurs : Wei Li, Auteur ; Jiabin Liu, Auteur ; Qian Du, Auteur Année de publication : 2016 Article en page(s) : pp 4094 - 4105 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] analyse en composantes principales
[Termes IGN] graphe
[Termes IGN] image hyperspectrale
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] valeur propreRésumé : (Auteur) Recently, sparse graph-based discriminant analysis (SGDA) has been developed for the dimensionality reduction and classification of hyperspectral imagery. In SGDA, a graph is constructed by ℓ1-norm optimization based on available labeled samples. Different from traditional methods (e.g., k-nearest neighbor with Euclidean distance), weights in an ℓ1-graph derived via a sparse representation can automatically select more discriminative neighbors in the feature space. However, the sparsity-based graph represents each sample individually, lacking a global constraint on each specific solution. As a consequence, SGDA may be ineffective in capturing the global structures of data. To overcome this drawback, a sparse and low-rank graph-based discriminant analysis (SLGDA) is proposed. Low-rank representation has been proved to be capable of preserving global data structures, although it may result in a dense graph. In SLGDA, a more informative graph is constructed by combining both sparsity and low rankness to maintain global and local structures simultaneously. Experimental results on several different multiple-class hyperspectral-classification tasks demonstrate that the proposed SLGDA significantly outperforms the state-of-the-art SGDA. Numéro de notice : A2016-879 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2536685 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2536685 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83042
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 7 (July 2016) . - pp 4094 - 4105[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)
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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'imageRésumé : (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
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 7 (July 2015) . - pp 3681 - 3693[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015071 RAB Revue Centre de documentation En réserve L003 Disponible Collaborative representation for hyperspectral anomaly detection / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)Permalink