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Multiple kernel learning based on discriminative kernel clustering for hyperspectral band selection / Jie Feng in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)
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Titre : Multiple kernel learning based on discriminative kernel clustering for hyperspectral band selection Type de document : Article/Communication Auteurs : Jie Feng, Auteur ; Licheng Jiao, Auteur ; Tao Sun, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 6516 - 6530 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification automatique
[Termes IGN] image hyperspectrale
[Termes IGN] intelligence artificielle
[Termes IGN] méthode fondée sur le noyauRésumé : (Auteur) In hyperspectral images, band selection plays a crucial role for land-cover classification. Multiple kernel learning (MKL) is a popular feature selection method by selecting the relevant features and classifying the images simultaneously. Unfortunately, a large number of spectral bands in hyperspectral images result in excessive kernels, which limit the application of MKL. To address this problem, a novel MKL method based on discriminative kernel clustering (DKC) is proposed. In the proposed method, a discriminative kernel alignment (KA) (DKA) is defined. Traditional KA measures kernel similarity independently of the current classification task. Compared with KA, DKA measures the similarity of discriminative information by introducing the comparison of intraclass and interclass similarities. It can evaluate both kernel redundancy and kernel synergy for classification. Then, DKA-based affinity-propagation clustering is devised to reduce the kernel scale and retain the kernels having high discrimination and low redundancy for classification. Additionally, an analysis of necessity for DKC in hyperspectral band selection is provided by empirical Rademacher complexity. Experimental results on several hyperspectral images demonstrate the effectiveness of the proposed band selection method in terms of classification performance and computation efficiency. Numéro de notice : A2016-915 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2585961 En ligne : https://doi.org/10.1109/TGRS.2016.2585961 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83140
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 11 (November 2016) . - pp 6516 - 6530[article]Robust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification / Zhi He in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)
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Titre : Robust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification Type de document : Article/Communication Auteurs : Zhi He, Auteur ; Lin Liu, Auteur Année de publication : 2016 Article en page(s) : pp 11 – 27 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification
[Termes IGN] décomposition d'image
[Termes IGN] image hyperspectrale
[Termes IGN] module d'extensionRésumé : (Auteur) Empirical mode decomposition (EMD) and its variants have recently been applied for hyperspectral image (HSI) classification due to their ability to extract useful features from the original HSI. However, it remains a challenging task to effectively exploit the spectral-spatial information by the traditional vector or image-based methods. In this paper, a three-dimensional (3D) extension of EMD (3D-EMD) is proposed to naturally treat the HSI as a cube and decompose the HSI into varying oscillations (i.e. 3D intrinsic mode functions (3D-IMFs)). To achieve fast 3D-EMD implementation, 3D Delaunay triangulation (3D-DT) is utilized to determine the distances of extrema, while separable filters are adopted to generate the envelopes. Taking the extracted 3D-IMFs as features of different tasks, robust multitask learning (RMTL) is further proposed for HSI classification. In RMTL, pairs of low-rank and sparse structures are formulated by trace-norm and l1,2l1,2-norm to capture task relatedness and specificity, respectively. Moreover, the optimization problems of RMTL can be efficiently solved by the inexact augmented Lagrangian method (IALM). Compared with several state-of-the-art feature extraction and classification methods, the experimental results conducted on three benchmark data sets demonstrate the superiority of the proposed methods. Numéro de notice : A2016--011 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.08.007 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.08.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83873
in ISPRS Journal of photogrammetry and remote sensing > vol 121 (November 2016) . - pp 11 – 27[article]Semi-supervised hyperspectral classification from a small number of training samples using a co-training approach / Michał Romaszewski in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)
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Titre : Semi-supervised hyperspectral classification from a small number of training samples using a co-training approach Type de document : Article/Communication Auteurs : Michał Romaszewski, Auteur ; Przemysław Głomb, Auteur ; Michał Cholewa, Auteur Année de publication : 2016 Article en page(s) : pp 60 – 76 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] classification automatique
[Termes IGN] détection de cible
[Termes IGN] données localisées
[Termes IGN] image hyperspectrale
[Termes IGN] performanceRésumé : (Auteur) We present a novel semi-supervised algorithm for classification of hyperspectral data from remote sensors. Our method is inspired by the Tracking-Learning-Detection (TLD) framework, originally applied for tracking objects in a video stream. TLD introduced the co-training approach called P-N learning, making use of two independent ‘experts’ (or learners) that scored samples in different feature spaces. In a similar fashion, we formulated the hyperspectral classification task as a co-training problem, that can be solved with the P-N learning scheme. Our method uses both spatial and spectral features of data, extending a small set of initial labelled samples during the process of region growing. We show that this approach is stable and achieves very good accuracy even for small training sets. We analyse the algorithm’s performance on several publicly available hyperspectral data sets. Numéro de notice : A2016--015 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.08.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.08.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83877
in ISPRS Journal of photogrammetry and remote sensing > vol 121 (November 2016) . - pp 60 – 76[article]A Computationally efficient algorithm for fusing multispectral and hyperspectral images / Raúl Guerra in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
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Titre : A Computationally efficient algorithm for fusing multispectral and hyperspectral images Type de document : Article/Communication Auteurs : Raúl Guerra, Auteur ; Sebastian Lopez, Auteur ; Roberto Sarmiento, Auteur Année de publication : 2016 Article en page(s) : pp 5712 - 5728 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de fusion
[Termes IGN] analyse combinatoire (maths)
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] qualité d'imageRésumé : (auteur) Remote sensing systems equipped with multispectral and hyperspectral sensors are able to capture images of the surface of the Earth at different wavelengths. In these systems, hyperspectral sensors typically provide images with a high spectral resolution but a reduced spatial resolution, while on the contrary, multispectral sensors are able to produce images with a rich spatial resolution but a poor spectral resolution. Due to this reason, different fusion algorithms have been proposed during the last years in order to obtain remotely sensed images with enriched spatial and spectral resolutions by wisely combining the data acquired for the same scene by multispectral and hyperspectral sensors. However, the algorithms so far proposed that are able to obtain fused images with a good spatial and spectral quality require a formidable amount of computationally complex operations that cannot be executed in parallel, which clearly prevent the utilization of these algorithms in applications under real-time constraints in which high-performance parallel-based computing systems are normally required for accelerating the overall process. On the other hand, there are other state-of-the-art algorithms that are capable of fusing these images with a lower computational effort but at the cost of decreasing the quality of the resultant fused image. In this paper, a new algorithm named computationally efficient algorithm for fusing multispectral and hyperspectral images (CoEf-MHI) is proposed in order to obtain a high-quality image from hyperspectral and multispectral images of the same scene with a low computational effort. The proposed CoEf-MHI algorithm is based on incorporating the spatial details of the multispectral image into the hyperspectral image, without introducing spectral distortions. To achieve this goal, the CoEf-MHI algorithm first spatially upsamples, by means of a bilinear interpolation, the input hyperspectral image to the spatial resolution of the input multispectral image, and then, it independently refines each pixel of the resulting image by linearly combining the multispectral and hyperspectral pixels in its neighborhood. The simulations performed in this work with different images demonstrate that our proposal is much more efficient than state-of-the-art approaches, being this efficiency understood as the ratio between the quality of the fused image and the computational effort required to obtain such image. Numéro de notice : A2016-860 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2570433 En ligne : https://doi.org/10.1109/TGRS.2016.2570433 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82889
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 10 (October 2016) . - pp 5712 - 5728[article]Deep feature extraction and classification of hyperspectral images based on convolutional neural networks / Yushi Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
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Titre : Deep feature extraction and classification of hyperspectral images based on convolutional neural networks Type de document : Article/Communication Auteurs : Yushi Chen, Auteur ; Hanlu Jiang, Auteur ; Chunyang Li, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 6232 - 6251 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection de cible
[Termes IGN] filtrage numérique d'image
[Termes IGN] image hyperspectrale
[Termes IGN] régularisation de Tychonoff
[Termes IGN] réseau neuronal convolutifRésumé : (auteur) Due to the advantages of deep learning, in this paper, a regularized deep feature extraction (FE) method is presented for hyperspectral image (HSI) classification using a convolutional neural network (CNN). The proposed approach employs several convolutional and pooling layers to extract deep features from HSIs, which are nonlinear, discriminant, and invariant. These features are useful for image classification and target detection. Furthermore, in order to address the common issue of imbalance between high dimensionality and limited availability of training samples for the classification of HSI, a few strategies such as L2 regularization and dropout are investigated to avoid overfitting in class data modeling. More importantly, we propose a 3-D CNN-based FE model with combined regularization to extract effective spectral-spatial features of hyperspectral imagery. Finally, in order to further improve the performance, a virtual sample enhanced method is proposed. The proposed approaches are carried out on three widely used hyperspectral data sets: Indian Pines, University of Pavia, and Kennedy Space Center. The obtained results reveal that the proposed models with sparse constraints provide competitive results to state-of-the-art methods. In addition, the proposed deep FE opens a new window for further research. Numéro de notice : A2016-869 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2584107 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2584107 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83026
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 10 (October 2016) . - pp 6232 - 6251[article]Evaluating EO1-Hyperion capability for mapping conifer and broadleaved forests / Nicola Puletti in European journal of remote sensing, vol 49 n° 1 (2016)PermalinkInfluence of tree species complexity on discrimination performance of vegetation indices / Azadeh Ghiyamat in European journal of remote sensing, vol 49 n° 1 (2016)PermalinkObject-based morphological profiles for classification of remote sensing imagery / Christian Geiss in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkSemisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning / Xiaorui Ma in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)PermalinkA tensor decomposition-based anomaly detection algorithm for hyperspectral image / Xing Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkEstimating forest species abundance through linear unmixing of CHRIS/PROBA imagery / S. Stagakis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)PermalinkGeometric calibration of a hyperspectral frame camera / Raquel A. de Oliveira in Photogrammetric record, vol 31 n° 155 (September - November 2016)PermalinkMapping of land cover in northern California with simulated hyperspectral satellite imagery / Matthew L. Clark in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)PermalinkNoise removal from hyperspectral image with joint spectral–spatial distributed sparse representation / Jie Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkRegression wavelet analysis for lossless coding of remote-sensing data / Naoufal Amrani in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)Permalink