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Multichannel Pulse-Coupled Neural Network-Based Hyperspectral Image Visualization / Puhong Duan in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2444 - 2456 Titre : Multichannel Pulse-Coupled Neural Network-Based Hyperspectral Image Visualization Type de document : Article/Communication Auteurs : Puhong Duan, Auteur ; Xudong Kang, Auteur ; Shutao Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2444 - 2456 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image numérique [Termes descripteurs IGN] analyse multibande [Termes descripteurs IGN] chromatopsie [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] cohérence des couleurs [Termes descripteurs IGN] image en couleur composée [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] synthèse trichromatique [Termes descripteurs IGN] visualisation de données Résumé : (auteur) Hyperspectral Image (HSI) visualization, which aims at displaying as much material information of original images as possible on a trichromatic monitor with natural color, plays an important role in image interpretation and analysis. However, most of the HSI visualization methods only focus on presenting the detail information of a scene without providing natural colors and distinguishing land covers with similar colors. In order to address this problem, this article proposes a multichannel pulse-coupled neural network (MPCNN)-based HSI visualization method, which consists of the following steps. First, the MPCNN is proposed and explored to fuse the original HSI so as to obtain a fused band with rich spatial details. Then, a color mapping scheme is proposed to determine the weights of red, green, and blue (RGB) channels. Finally, the weighted RGB channels are stacked together for visualization. Experiments performed on four hyperspectral data sets demonstrate that the proposed method not only displays the HSI with nature colors but also improves the details in the image. The effectiveness of the proposed method is demonstrated in terms of both visual effect and objective indexes. Numéro de notice : A2020-197 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2949427 date de publication en ligne : 20/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2949427 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94867 [article]

Random-walker-based collaborative learning for hyperspectral image classification / Bin Sun in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 1 (January 2017) . - pp 212 - 222 Titre : Random-walker-based collaborative learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Bin Sun, Auteur ; Xudong Kang, Auteur ; Shutao Li, Auteur ; Jon Atli Benediktsson, Auteur Année de publication : 2017 Article en page(s) : pp 212 - 222 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage semi-dirigé [Termes descripteurs IGN] champ aléatoire de Markov [Termes descripteurs IGN] classification [Termes descripteurs IGN] Extended random walker [Termes descripteurs IGN] image hyperspectrale Résumé : (Auteur) Active learning (AL) and semisupervised learning (SSL) are both promising solutions to hyperspectral image classification. Given a few initial labeled samples, this work combines AL and SSL in a novel manner, aiming to obtain more manually labeled and pseudolabeled samples and use them together with the initial labeled samples to improve the classification performance. First, based on a comparison of the segmentation and spectral-spatial classification results obtained by random walker (RW) and extended RW (ERW) algorithms, the unlabeled samples are separated into two different sets, i.e., low- and high-confidence unlabeled data sets. For the high-confidence unlabeled data, pseudolabeling is performed, which can ensure the correctness and informativeness of the pseudolabeled samples. For the low-confidence unlabeled data, AL is used to select samples. In this way, the samples which are more effective for improvement of classification performance can be labeled in only a few iterations. Finally, with the learned training set and the original hyperspectral image as inputs, the ERW classifier is used to obtain the final classification result. Experiments performed on three real hyperspectral data sets show that the proposed method can achieve competitive classification accuracy even with a very limited number of manually labeled samples. Numéro de notice : A2017-019 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi./org/10.1109/TGRS.2016.2604290 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83950 [article]

Spectral–spatial classification of hyperspectral images with a superpixel-based discriminative sparse model / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 8 (August 2015)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 8 (August 2015) . - pp 4186 - 4201 Titre : Spectral–spatial classification of hyperspectral images with a superpixel-based discriminative sparse model Type de document : Article/Communication Auteurs : Leyuan Fang, Auteur ; S. Li, Auteur ; Xudong Kang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 4186 - 4201 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme d'apprentissage [Termes descripteurs IGN] analyse discriminante [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification spectrale [Termes descripteurs IGN] image hyperspectrale Résumé : (Auteur) A novel superpixel-based discriminative sparse model (SBDSM) for spectral-spatial classification of hyperspectral images (HSIs) is proposed. Here, a superpixel in a HSI is considered as a small spatial region whose size and shape can be adaptively adjusted for different spatial structures. In the proposed approach, the SBDSM first clusters the HSI into many superpixels using an efficient oversegmentation method. Then, pixels within each superpixel are jointly represented by a set of common atoms from a dictionary via a joint sparse regularization. The recovered sparse coefficients are utilized to determine the class label of the superpixel. In addition, instead of directly using a large number of sampled pixels as dictionary atoms, the SBDSM applies a discriminative K-SVD learning algorithm to simultaneously train a compact representation dictionary, as well as a discriminative classifier. Furthermore, by utilizing the class label information of training pixels and dictionary atoms, a class-labeled orthogonal matching pursuit is proposed to accelerate the K-SVD algorithm while still enforcing high discriminability on sparse coefficients when training the classifier. Experimental results on four real HSI datasets demonstrate the superiority of the proposed SBDSM algorithm over several well-known classification approaches in terms of both classification accuracies and computational speed. Numéro de notice : A2015-384 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76859 [article]

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Extended random walker-based classification of hyperspectral images / Xudong Kang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 1 (January 2015) . - pp 144 - 153 Titre : Extended random walker-based classification of hyperspectral images Type de document : Article/Communication Auteurs : Xudong Kang, Auteur ; Shutao Li, Auteur ; Leyuan Fang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 144 - 153 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] classification spectrale [Termes descripteurs IGN] Extended random walker [Termes descripteurs IGN] graphe [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] segmentation d'image Résumé : (Auteur) This paper introduces a novel spectral-spatial classification method for hyperspectral images based on extended random walkers (ERWs), which consists of two main steps. First, a widely used pixelwise classifier, i.e., the support vector machine (SVM), is adopted to obtain classification probability maps for a hyperspectral image, which reflect the probabilities that each hyperspectral pixel belongs to different classes. Then, the obtained pixelwise probability maps are optimized with the ERW algorithm that encodes the spatial information of the hyperspectral image in a weighted graph. Specifically, the class of a test pixel is determined based on three factors, i.e., the pixelwise statistics information learned by a SVM classifier, the spatial correlation among adjacent pixels modeled by the weights of graph edges, and the connectedness between the training and test samples modeled by random walkers. Since the three factors are all well considered in the ERW-based global optimization framework, the proposed method shows very good classification performances for three widely used real hyperspectral data sets even when the number of training samples is relatively small. Numéro de notice : A2015-030 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2319373 En ligne : https://doi.org/10.1109/TGRS.2014.2319373 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75111 [article]

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Spectral–spatial hyperspectral image classification via multiscale adaptive sparse representation / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 12 (December 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 12 (December 2014) . - pp 7738 - 7749 Titre : Spectral–spatial hyperspectral image classification via multiscale adaptive sparse representation Type de document : Article/Communication Auteurs : Leyuan Fang, Auteur ; Shutao Li, Auteur ; Xudong Kang, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 7738 - 7749 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] analyse multiéchelle [Termes descripteurs IGN] classification spectrale [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] Représentation incomplète Résumé : (Auteur) Sparse representation has been demonstrated to be a powerful tool in classification of hyperspectral images (HSIs). The spatial context of an HSI can be exploited by first defining a local region for each test pixel and then jointly representing pixels within each region by a set of common training atoms (samples). However, the selection of the optimal region scale (size) for different HSIs with different types of structures is a nontrivial task. In this paper, considering that regions of different scales incorporate the complementary yet correlated information for classification, a multiscale adaptive sparse representation (MASR) model is proposed. The MASR effectively exploits spatial information at multiple scales via an adaptive sparse strategy. The adaptive sparse strategy not only restricts pixels from different scales to be represented by training atoms from a particular class but also allows the selected atoms for these pixels to be varied, thus providing an improved representation. Experiments on several real HSI data sets demonstrate the qualitative and quantitative superiority of the proposed MASR algorithm when compared to several well-known classifiers. Numéro de notice : A2014-639 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2317499 En ligne : https://doi.org/10.1109/TGRS.2014.2317499 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75076 [article]

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Feature extraction of hyperspectral images with image fusion and recursive filtering / Xudong Kang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)

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