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Robust sparse hyperspectral unmixing with ℓ2,1 norm / Yong Ma in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
[article]
Titre : Robust sparse hyperspectral unmixing with ℓ2,1 norm Type de document : Article/Communication Auteurs : Yong Ma, Auteur ; Chang Li, Auteur ; Xiaoguang Mei, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1227 - 1239 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] image hyperspectrale
[Termes IGN] matrice creuse
[Termes IGN] méthode robuste
[Termes IGN] pondérationRésumé : (Auteur) Sparse unmixing (SU) of hyperspectral data have recently received particular attention for analyzing remote sensing images, which aims at finding the optimal subset of signatures to best model the mixed pixel in the scene. However, most SU methods are based on the commonly admitted linear mixing model, which ignores the possible nonlinear effects (i.e., nonlinearity), and the nonlinearity is merely treated as outlier. Besides, the traditional SU algorithms often adopt the ℓ2 norm loss function, which makes them sensitive to noises and outliers. In this paper, we propose a robust SU (RSU) method with ℓ2,1 norm loss function, which is robust for noises and outliers. Then, the RSU can be solved by the alternative direction method of multipliers. Finally, the experiments on both synthetic data sets and real hyperspectral images demonstrate that the proposed RSU is efficient for solving the hyperspectral SU problem compared with the state-of-the-art algorithms. Numéro de notice : A2017-150 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2616161 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2616161 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84681
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 3 (March 2017) . - pp 1227 - 1239[article]Spatial-spectral unsupervised convolutional sparse auto-encoder classifier for hyperspectral imagery / Xiaobing Han in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)
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Titre : Spatial-spectral unsupervised convolutional sparse auto-encoder classifier for hyperspectral imagery Type de document : Article/Communication Auteurs : Xiaobing Han, Auteur ; Yanfei Zhong, Auteur ; Liangpei Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 195 - 206 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classificateur non paramétrique
[Termes IGN] cohérence (physique)
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectraleRésumé : (Auteur) The traditional spatial-spectral classification methods applied to hyperspectral remote sensing imagery are conducted by combining the spatial information vector and the spectral information vector in a separate manner, which may cause information loss and concatenation deficiency between the spatial and spectral information. In addition, the traditional morphological-based spatial-spectral classification methods require the design of handcrafted features according to experience, which is far from automatic and lacks generalization ability. To automatically represent the spatial-spectral features around the central pixel within a spatial neighborhood window, a novel spatial-spectral feature classification method based on the unsupervised convolutional sparse auto-encoder (UCSAE) with a window-in-window strategy is proposed in this study. The UCSAE algorithm features a unique spatial-spectral feature extraction approach which is executed in two stages. The first stage represents the spatial-spectral features within a spatial neighborhood window on the basis of spatial-spectral feature extraction of sub-windows with a sparse auto-encoder (SAE). The second stage exploits the spatial-spectral feature representation with a convolution mechanism for the larger outer windows. The UCSAE algorithm was validated by two widely used hyperspectral imagery datasets (the Pavia University dataset and the Washington DC Mall dataset) obtaining accuracies of 90.03 percent and 96.88 percent, respectively, which are better results than those obtained by the traditional hyperspectral spatial-spectral classification approaches. Numéro de notice : A2017-088 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.3.195 En ligne : https://doi.org/10.14358/PERS.83.3.195 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84423
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 3 (March 2017) . - pp 195 - 206[article]Adaptive spectral–spatial compression of hyperspectral image with sparse representation / Wei Fu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)
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Titre : Adaptive spectral–spatial compression of hyperspectral image with sparse representation Type de document : Article/Communication Auteurs : Wei Fu, Auteur ; Shutao Li, Auteur ; Leyuan Fang, Auteur ; Jon Atli Benediktsson, Auteur Année de publication : 2017 Article en page(s) : pp 671 - 682 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] codage
[Termes IGN] compression d'image
[Termes IGN] image hyperspectrale
[Termes IGN] pixel
[Termes IGN] représentation parcimonieuse
[Termes IGN] zone homogèneRésumé : (Auteur) Sparse representation (SR) can transform spectral signatures of hyperspectral pixels into sparse coefficients with very few nonzero entries, which can efficiently be used for compression. In this paper, a spectral-spatial adaptive SR (SSASR) method is proposed for hyperspectral image (HSI) compression by taking advantage of the spectral and spatial information of HSIs. First, we construct superpixels, i.e., homogeneous regions with adaptive sizes and shapes, to describe HSIs. Since homogeneous regions usually consist of similar pixels, pixels within each superpixel will be similar and share similar spectral signatures. Then, the spectral signatures of each superpixel can be simultaneously coded in the SR model to exploit their joint sparsity. Since different superpixels generally have different performances of SR, their rate-distortion performances in the sparse coding will be different. To achieve the best possible overall rate-distortion performance, an adaptive coding scheme is introduced to adaptively assign distortions to superpixels. Finally, the obtained sparse coefficients are quantized and entropy coded and constitute the final bitstream with the coded superpixel map. The experimental results over several HSIs show that the proposed SSASR method outperforms some state-of-the-art HSI compression methods in terms of the rate-distortion and spectral fidelity performances. Numéro de notice : A2017-141 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2613848 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2613848 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84629
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 2 (February 2017) . - pp 671 - 682[article]Characterizing vegetation canopy structure using airborne remote sensing data / Debsunder Dutta in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)
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Titre : Characterizing vegetation canopy structure using airborne remote sensing data Type de document : Article/Communication Auteurs : Debsunder Dutta, Auteur ; Kunxuan Wang, Auteur ; Esther Lee, Auteur Année de publication : 2017 Article en page(s) : pp 1160 - 1178 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] canopée
[Termes IGN] densité de la végétation
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] feuille (végétation)
[Termes IGN] forêt ripicole
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] semis de points
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] voxelRésumé : (Auteur) Vegetation canopy structure plays an important role in the partitioning of incident solar radiation, photosynthesis, transpiration, and other scalar fluxes. The vertical foliage distribution of the plant canopy is represented by leaf area density (LAD), which is defined as the one-sided leaf area per unit volume. Airborne light detection and ranging (LiDAR) offers the possibility to characterize the 3-D variation of LAD over space, which still remains a challenge to estimate. Moreover, the low density of point cloud data generally offered by airborne LiDAR may be insufficient for accurate LAD estimation in dense overlapping forest canopies. We develop a method for the estimation of the LAD profile using a combination of airborne LiDAR and hyperspectral data using a feature-based data fusion approach. After identifying vegetation species using hyperspectral data, point cloud LiDAR data is used in a “tree-shaped” voxel approach to characterize the LAD of trees in a riparian forest setting. We also propose a set of relationships on simple geometry of overlap for the construction of tree shaped voxels. In a forest setting with overlapping canopies, the results indicate that the tree-shaped voxels are better able to attribute the LAD to the upper and middle parts of the overall canopy as well as individual tall and short trees compared with traditional cylindrical voxels. Numéro de notice : A2017-147 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2620478 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2620478 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84635
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 2 (February 2017) . - pp 1160 - 1178[article]Joint sparse representation and multitask learning for hyperspectral target detection / Yuxiang Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)
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Titre : Joint sparse representation and multitask learning for hyperspectral target detection Type de document : Article/Communication Auteurs : Yuxiang Zhang, Auteur ; Bo Du, Auteur ; Liangpei Zhang, Auteur ; Tongliang Liu, Auteur Année de publication : 2017 Article en page(s) : pp 894 - 906 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] détection de cible
[Termes IGN] image hyperspectrale
[Termes IGN] représentation parcimonieuseRésumé : (Auteur) With the high spectral resolution, hyperspectral images (HSIs) provide great potential for target detection, which is playing an increasingly important role in HSI processing. Many target detection methods uniformly utilize all the spectral information or employ reduced spectral information to distinguish the targets and background. Simultaneously reducing spectral redundancy and preserving the discriminative information is a challenging problem in hyperspectral target detection. The multitask learning (MTL) technique may have the potential to solve the above problem, since it can explore the redundancy knowledge to construct multiple sub-HSIs and integrate them without any information loss. This paper proposes the joint sparse representation and MTL (JSR-MTL) method for hyperspectral target detection. This approach: 1) explores the HSIs similarity by a band cross-grouping strategy to construct multiple sub-HSIs; 2) takes full advantage of the MTL technique to integrate the sparse representation models for the multiple related sub-HSIs; and 3) applies the total reconstruction error difference accumulated over all the tasks to detect the targets. Extensive experiments were carried out on three HSIs, and it was founded that JSR-MTL generally shows a better detection performance than the other target detection methods. Numéro de notice : A2017-144 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2616649 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2616649 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84632
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 2 (February 2017) . - pp 894 - 906[article]Multi-objective based spectral unmixing for hyperspectral images / Xia Xu in ISPRS Journal of photogrammetry and remote sensing, vol 124 (February 2017)PermalinkComputationally efficient hyperspectral data learning based on the doubly stochastic dirichlet process / Xing Sun in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)PermalinkFusion of graph embedding and sparse representation for feature extraction and classification of hyperspectral imagery / Fulin Luo in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 1 (January 2017)PermalinkHyperspectral image classification with canonical correlation forests / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)PermalinkModeling spatial and temporal variabilities in hyperspectral image unmixing / Pierre-Antoine Thouvenin (2017)PermalinkRandom-walker-based collaborative learning for hyperspectral image classification / Bin Sun in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)PermalinkTélédétection pour l'observation des surfaces continentales, Volume 1. Observation des surfaces continentales par télédétection optique / Nicolas Baghdadi (2017)PermalinkA two-step decision fusion strategy: application to hyperspectral and multispectral images for urban classification / Walid Ouerghemmi (2017)PermalinkUrban objects classification by spectral library: Feasibility and applications / Walid Ouerghemmi (2017)PermalinkClass-specific sparse multiple kernel learning for spectral–spatial hyperspectral image classification / Tianzhu Liu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)Permalink