Descripteur
Termes IGN > sciences naturelles > physique > traitement d'image > analyse d'image numérique > analyse des mélanges spectraux
analyse des mélanges spectrauxSynonyme(s)SMA démélange spectral |
Documents disponibles dans cette catégorie (133)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution / Zhenfeng Shao in Geo-spatial Information Science, vol 25 n° 4 (December 2022)
[article]
Titre : Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution Type de document : Article/Communication Auteurs : Zhenfeng Shao, Auteur ; Yuan Zhang, Auteur ; Cheng Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 550 - 567 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de mélange spectral d’extrémités multiples
[Termes IGN] approche hiérarchique
[Termes IGN] Chine
[Termes IGN] distribution spatiale
[Termes IGN] image Gaofen
[Termes IGN] image Landsat-OLI
[Termes IGN] scène urbaine
[Termes IGN] surface imperméableRésumé : (auteur) Impervious surface mapping is essential for urban environmental studies. Spectral Mixture Analysis (SMA) and its extensions are widely employed in impervious surface estimation from medium-resolution images. For SMA, inappropriate endmember combinations and inadequate endmember classes have been recognized as the primary reasons for estimation errors. Meanwhile, the spectral-only SMA, without considering urban spatial distribution, fails to consider spectral variability in an adequate manner. The lack of endmember class diversity and their spatial variations lead to over/underestimation. To mitigate these issues, this study integrates a hierarchical strategy and spatially varied endmember spectra to map impervious surface abundance, taking Wuhan and Wuzhou as two study areas. Specifically, the piecewise convex multiple-model endmember detection algorithm is applied to automatically hierarchize images into three regions, and distinct endmember combinations are independently developed in each region. Then, spatially varied endmember spectra are synthesized through neighboring spectra using the distance-based weight. Comparative analysis indicates that the proposed method achieves better performance than Hierarchical SMA and Fixed Four-endmembers SMA in terms of MAE, SE, and RMSE. Further analysis suggests that the hierarchical strategy can expand endmember class types and considerably improve the performance for the study areas in general, specifically in less developed areas. Moreover, we find that spatially varied endmember spectra facilitate the reduction of heterogeneous surface material variations and achieve the improved performance in developed areas. Numéro de notice : A2022-890 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10095020.2022.2028535 Date de publication en ligne : 02/03/2022 En ligne : https://doi.org/10.1080/10095020.2022.2028535 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102237
in Geo-spatial Information Science > vol 25 n° 4 (December 2022) . - pp 550 - 567[article]Mapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression / Haoyu Wang in Remote sensing of environment, vol 278 (September 2022)
[article]
Titre : Mapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression Type de document : Article/Communication Auteurs : Haoyu Wang, Auteur ; Xiuyuan Zhang, Auteur ; Shihong Du, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113088 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] apprentissage profond
[Termes IGN] carte d'occupation du sol
[Termes IGN] cartographie thématique
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] croissance urbaine
[Termes IGN] image Terra-MODIS
[Termes IGN] modèle de régression
[Termes IGN] série temporelle
[Termes IGN] surface cultivéeRésumé : (auteur) Global urbanization changes land cover patterns and affects the living environment of humans. However, urbanization and its evolution process, i.e., conversions among diverse land covers, are hard to measure, as existing land cover maps usually have low temporal resolutions; conversely, long-term and temporally dense land cover maps, such as vegetation-impervious-soil decomposition maps base on MODIS, ignore the important land cover of cropland in urban evolution process (UEP). To resolve the issue, this study suggests a novel model named time-extended non-crop vegetation-impervious-cropland (Time V-I-C) to represent and quantify different stages of UEP; then, a normalized multi-objective T-ConvLSTM (NMT) method is proposed to unmix cropland, non-crop vegetation, and impervious based on the intra-annual remotely-sensed time series, and obtain their fractions in each pixel for generating UEP maps. Consequently, UEP maps from 2001 to 2018 are generated for two Chinese urban agglomerations, i.e., Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations. The mapping results have high accuracies with a small standard error of regression (SER) of 13.1%, small root mean square error (RMSE) of 12.6%, and small mean absolute error (MAE) of 8.4%, and the maps reveal the different UEP in the two urban agglomerations. Therefore, this study provides a new idea for expressing UEP and contributes to a wide range of urbanization studies and sustainable city development. Numéro de notice : A2022-511 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.rse.2022.113088 Date de publication en ligne : 25/05/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113088 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101049
in Remote sensing of environment > vol 278 (September 2022) . - n° 113088[article]Detection of potential gold mineralization areas using MF-fuzzy approach on multispectral data / Tohid Nouri in Geocarto international, Vol 37 n° 17 ([20/08/2022])
[article]
Titre : Detection of potential gold mineralization areas using MF-fuzzy approach on multispectral data Type de document : Article/Communication Auteurs : Tohid Nouri, Auteur Année de publication : 2022 Article en page(s) : pp 5017 - 5040 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] altération géologique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] appariement d'images
[Termes IGN] diffraction
[Termes IGN] image multibande
[Termes IGN] Iran
[Termes IGN] logique floue
[Termes IGN] mine d'or
[Termes IGN] MNS ASTER
[Termes IGN] pixel
[Termes IGN] prospection minérale
[Termes IGN] sédiment
[Termes IGN] spectrométrieRésumé : (auteur) The northeast area of Ardabil, a city located in northwestern Iran, is one of the potential gold mineralization areas. In this study, ASTER data were used to identify the alteration events in this region. For this purpose, a novel approach was used in which the fuzzy logic was implemented to extract the co-occurrence map of the endmembers. This method revealed alterations more accurately than SID. Stream sediment samples were employed to validate the obtained results. Since these samples are alluvial, their catchment basins were determined and overlaid with the alteration maps. To the best of the authors’ knowledge, this validation approach has not been used in previous studies. The extracted alteration zones were in high conformity to the stream sediment samples. Next, X-ray diffraction (XRD) analysis and field spectrometry were used for delineation of the mineralogical phases present in the anomalous areas. Finally, the potential gold mineralization zones were identified. Numéro de notice : A2022-701 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1903575 Date de publication en ligne : 07/06/2021 En ligne : https://doi.org/10.1080/10106049.2021.1903575 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101560
in Geocarto international > Vol 37 n° 17 [20/08/2022] . - pp 5017 - 5040[article]Hyperspectral unmixing using transformer network / Preetam Ghosh in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)
[article]
Titre : Hyperspectral unmixing using transformer network Type de document : Article/Communication Auteurs : Preetam Ghosh, Auteur ; Swalpa Kumar Roy, Auteur ; Bikram Koirala, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5535116 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image hyperspectraleRésumé : (auteur) Transformers have intrigued the vision research community with their state-of-the-art performance in natural language processing. With their superior performance, transformers have found their way into the field of hyperspectral image classification and achieved promising results. In this article, we harness the power of transformers to conquer the task of hyperspectral unmixing and propose a novel deep neural network-based unmixing model with transformers. A transformer network captures nonlocal feature dependencies by interactions between image patches, which are not employed in convolutional neural network (CNN) models, and hereby has the ability to enhance the quality of the endmember spectra and the abundance maps. The proposed model is a combination of a convolutional autoencoder and a transformer. The hyperspectral data is encoded by the convolutional encoder. The transformer captures long-range dependencies between the representations derived from the encoder. The data are reconstructed using a convolutional decoder. We applied the proposed unmixing model to three widely used unmixing datasets, that is, Samson, Apex, and Washington DC Mall, and compared it with the state-of-the-art in terms of root mean squared error and spectral angle distance. The source code for the proposed model will be made publicly available at https://github.com/preetam22n/DeepTrans-HSU . Numéro de notice : A2022-662 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3196057 Date de publication en ligne : 03/08/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3196057 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101518
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 8 (August 2022) . - n° 5535116[article]A new method to detect targets in hyperspectral images based on principal component analysis / Shahram Sharifi Hashjin in Geocarto international, vol 37 n° 9 ([15/05/2022])
[article]
Titre : A new method to detect targets in hyperspectral images based on principal component analysis Type de document : Article/Communication Auteurs : Shahram Sharifi Hashjin, Auteur ; Safa Khazai, Auteur Année de publication : 2022 Article en page(s) : pp 2679 - 2697 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] analyse en composantes principales
[Termes IGN] détection de cible
[Termes IGN] estimation de cohérence
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectraleRésumé : (auteur) Target detection (TD) is a major task in hyperspectral image (HSI) processing which, due to the high spectral resolution, requires dealing with the curse of dimensionality. The integrated feature extraction and selection is a well-known solution for dimensionality reduction of HSIs. In this study, a new method is presented to improve the performance of TD algorithms based on principal component analysis (PCA) feature space. In this method, using the implantation of the target spectrum (TS) in the HSI and following the simulated targets in the PCA feature space, the best principal components (PCs) are selected. Then, using the mixing and unmixing coefficients of the PCs, a new TS and a new image in the PCA feature space are created. Afterwards, using the new spectrum of the target, the TD algorithm is run on the new HSI. The performance of the proposed method is compared to nine counterpart algorithms on Hymap and Hyperion HSI. All the comparisons are performed using adaptive coherence estimator (ACE) TD algorithm. Experimental results illustrate that the proposed method, compared to its counterparts, yields superior performance based on the false alarm rate (FAR) measure. It gives an average FAR value of about 16, which is approximately 9% better than that of its best counterparts. Numéro de notice : A2022-568 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1831625 Date de publication en ligne : 01/12/2020 En ligne : https://doi.org/10.1080/10106049.2020.1831625 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101251
in Geocarto international > vol 37 n° 9 [15/05/2022] . - pp 2679 - 2697[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2022091 RAB Revue Centre de documentation En réserve L003 Disponible Unmixing-based spatiotemporal image fusion accounting for complex land cover changes / Xiaolu Jiang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 5 (May 2022)PermalinkDeep generative model for spatial–spectral unmixing with multiple endmember priors / Shuaikai Shi in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)PermalinkSimultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3 / Nima Pahlevan in Remote sensing of environment, vol 270 (March 2022)PermalinkCharacteristics of taiga and tundra snowpack in development and validation of remote sensing of snow / Henna-Reetta Hannula (2022)PermalinkImproving LSMA for impervious surface estimation in an urban area / Jin Wang in European journal of remote sensing, vol 55 n° 1 (2022)PermalinkA novel unmixing-based hypersharpening method via convolutional neural network / Xiaochen Lu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)PermalinkPhase unmixing of TerraSAR-X staring spotlight interferograms in building scale for PS height and deformation / Peng Liu in ISPRS Journal of photogrammetry and remote sensing, vol 180 (October 2021)PermalinkFluvial gravel bar mapping with spectral signal mixture analysis / Liza Stančič in European journal of remote sensing, vol 54 sup 1 (2021)PermalinkCloud-native seascape mapping of Mozambique’s Quirimbas National Park with Sentinel-2 / Dimitris Poursanidis in Remote sensing in ecology and conservation, vol 7 n° 2 (June 2021)PermalinkCorrentropy-based spatial-spectral robust sparsity-regularized hyperspectral unmixing / Xiaorun Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)PermalinkLearning disentangled representations of satellite image time series in a weakly supervised manner / Eduardo Hugo Sanchez (2021)PermalinkSpectral variability in hyperspectral unmixing : Multiscale, tensor, and neural network-based approaches / Ricardo Augusto Borsoi (2021)PermalinkUnmixing-based Sentinel-2 downscaling for urban land cover mapping / Fei Xu in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)PermalinkMapping tree species deciduousness of tropical dry forests combining reflectance, spectral unmixing, and texture data from high-resolution imagery / Astrid Helena Huechacona-Ruiz in Forests, vol 11 n°11 (November 2020)PermalinkHyperspectral unmixing using orthogonal sparse prior-based autoencoder with hyper-laplacian loss and data-driven outlier detection / Zeyang Dou in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)PermalinkMonitoring narrow mangrove stands in Baja California Sur, Mexico using linear spectral unmixing / Jonathan B. Thayn in Marine geodesy, Vol 43 n° 5 (September 2020)PermalinkA novel nonlinear hyperspectral unmixing approach for images of oil spills at sea / Ying Li in International Journal of Remote Sensing IJRS, vol 41 n° 12 (20 - 30 March 2020)PermalinkAssessing environmental impacts of urban growth using remote sensing / John C. Trinder in Geo-spatial Information Science, vol 23 n° 1 (March 2020)Permalink10th Colour and Visual Computing Symposium 2020 (CVCS 2020), Gjøvik, Norway, and Virtual, September 16-17, 2020 / Jean-Baptiste Thomas (2020)PermalinkPotential of Landsat-8 and Sentinel-2A composite for land use land cover analysis / Divyesh Varade in Geocarto international, vol 34 n° 14 ([30/10/2019])PermalinkPartial linear NMF-based unmixing methods for detection and area estimation of photovoltaic panels in urban hyperspectral remote sensing data / Moussa Sofiane Karoui in Remote sensing, vol 11 n° 18 (September 2019)PermalinkBurn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data / Alfonso Fernández-Manso in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)PermalinkHyperspectral analysis of soil polluted with four types of hydrocarbons / Laura A. Reséndez-Hernández in Geocarto international, vol 34 n° 9 ([15/06/2019])Permalink3D hyperspectral point cloud generation: Fusing airborne laser scanning and hyperspectral imaging sensors for improved object-based information extraction / Maximilian Brell in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)PermalinkSpectral unmixing with perturbed endmembers / Reza Arablouei in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)PermalinkUrban impervious surface estimation from remote sensing and social data / Yan Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 12 (December 2018)PermalinkUnmixing polarimetric radar images based on land cover type identified by higher resolution optical data before target decomposition: application to forest and bare soil / Sébastien Giordano in IEEE Transactions on geoscience and remote sensing, vol 56 n° 10 (October 2018)PermalinkPredicting temperate forest stand types using only structural profiles from discrete return airborne lidar / Melissa Fedrigo in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)PermalinkDetection and area estimation for photovoltaic panels in urban hyperspectral remote sensing data by an original NMF-based unmixing method / Moussa Sofiane Karoui (2018)PermalinkPermalinkRobust minimum volume simplex analysis for hyperspectral unmixing / Shaoquan Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)PermalinkSparse distributed multitemporal hyperspectral unmixing / Jakob Sigurdsson in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)PermalinkSpatial group sparsity regularized nonnegative matrix factorization for hyperspectral unmixing / Xinyu Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)PermalinkFrom subpixel to superpixel : a novel fusion framework for hyperspectral image classification / Ting Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkA novel preunmixing framework for efficient detection of linear mixtures in hyperspectral images / Andrea Marinoni in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkJoint hyperspectral superresolution and unmixing with interactive feedback / Chen Yi in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)PermalinkTotal variation regularized reweighted sparse nonnegative matrix factorization for hyperspectral unmixing / Wei He in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)PermalinkMultilayer NMF for blind unmixing of hyperspectral imagery with additional constraints / L. Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 4 (April 2017)PermalinkAdaptive linear spectral mixture analysis / Chein-I Chang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkRobust sparse hyperspectral unmixing with ℓ2,1 norm / Yong Ma in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkMulti-objective based spectral unmixing for hyperspectral images / Xia Xu in ISPRS Journal of photogrammetry and remote sensing, vol 124 (February 2017)PermalinkModeling spatial and temporal variabilities in hyperspectral image unmixing / Pierre-Antoine Thouvenin (2017)PermalinkMultiband image fusion based on spectral unmixing / Qi Wei in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)PermalinkBlind hyperspectral unmixing using total variation and ℓq sparse regularization / Jakob Sigurdsson in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 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)PermalinkRobust collaborative nonnegative matrix factorization for hyperspectral unmixing / Jun Li 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)PermalinkSemiblind hyperspectral unmixing in the presence of spectral library mismatches / Xiao Fu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkSimultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing / Paris V. Giampouras in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)PermalinkA superresolution land-cover change detection method using remotely sensed images with different spatial resolutions / Xiaodong Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkUnsupervised multitemporal spectral unmixing for detecting multiple changes in hyperspectral images / Sicong Liu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)PermalinkComparative analysis on utilisation of linear spectral unmixing and band ratio methods for processing ASTER data to delineate bauxite over a part of Chotonagpur plateau, Jharkhand, India / Arindam Guha in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)PermalinkThin cloud removal based on signal transmission principles and spectral mixture analysis / Meng Xu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)PermalinkUniformity-based superpixel segmentation of hyperspectral images / Arun M. Saranathan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)PermalinkApport de la prise en compte de la variabilité intra-classe dans les méthodes de démélange hyperspectral pour l'imagerie urbaine / Charlotte Revel (2016)PermalinkA multilinear mixing model for nonlinear spectral unmixing / Rob Heylen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)PermalinkPermalinkLeveraging in-scene spectra for vegetation species discrimination with MESMA-MDA / Brian D. Bue in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)PermalinkOn diverse noises in hyperspectral unmixing / Chunzhi Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)PermalinkTwo dimensional linear discriminant analyses for hyperspectral data / Maryam Imani in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 10 (October 2015)PermalinkMinimum volume simplex analysis: A fast algorithm for linear hyperspectral unmixing / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)PermalinkOn spectral unmixing resolution using extended support vector machines / Xiaofeng Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)PermalinkA novel negative abundance‐oriented hyperspectral unmixing algorithm / Rubén Marrero in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)PermalinkSubstance dependence constrained sparse NMF for hyperspectral unmixing / Yuan Yuan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)PermalinkComplementarity of discriminative classifiers and spectral unmixing techniques for the interpretation of hyperspectral images / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)PermalinkLinear spectral mixture analysis via multiple-kernel learning for hyperspectral image classification / Keng-Hao Liu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)PermalinkA physics-based unmixing method to estimate subpixel temperatures on mixed pixels / Manuel Cubero-Castan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)PermalinkAn adaptive subpixel mapping method based on MAP model and class determination strategy for hyperspectral remote sensing imagery / Yanfei Zhong in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)PermalinkConstrained least squares algorithms for nonlinear unmixing of hyperspectral imagery / Hanye Pu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)PermalinkSparse unmixing of hyperspectral data using spectral a priori information / Wei Tang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)PermalinkAn abundance characteristic-based independent component analysis for hyperspectral unmixing / Nan Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)PermalinkAssessment of the relevance of information derived from the unmixing of polarimetric radar images / Sébastien Giordano (2015)PermalinkDémélange d’images radar polarimétrique par séparation thématique de sources / Sébastien Giordano (2015)PermalinkAdaptive non-local Euclidean medians sparse unmixing for hyperspectral imagery / Ruyi Feng in ISPRS Journal of photogrammetry and remote sensing, vol 97 (November 2014)PermalinkEstimating fractional land cover in semi-arid central Kalahari: the impact of mapping method (spectral unmixing vs. object-based image analysis) and vegetation morphology / Niti B. Mishra in Geocarto international, vol 29 n° 7 - 8 (November - December 2014)PermalinkHyperspectral unmixing with [lq] regularization / Jakob Sigurdsson in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)PermalinkHyperspectral image resolution enhancement using high-resolution multispectral image based on spectral unmixing / Mohamed Amine Bendoumi in IEEE Transactions on geoscience and remote sensing, vol 52 n° 10 tome 2 (October 2014)PermalinkIntegration of Lidar and Landsat to estimate forest canopy cover in coastal British Columbia / Oumer S. Ahmed in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 10 (October 2014)PermalinkRegularized simultaneous forward–backward greedy algorithm for sparse unmixing of hyperspectral data / Wei Tang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 9 Tome 1 (September 2014)PermalinkSubspace matching pursuit for sparse unmixing of hyperspectral data / Zhenwei Shi in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 1 (June 2014)PermalinkDouble constrained NMF for hyperspectral unmixing / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)PermalinkProgressive band selection of spectral unmixing for hyperspectral imagery / Chein-I Chang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)PermalinkAdaptive subpixel mapping based on a multiagent system for remote-sensing imagery / Xiong Xu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)PermalinkDetecting subcanopy invasive plant species in tropical rainforest by integrating optical and microwave (InSAR/PolInSAR) remote sensing data, and a decision tree algorithm / Abduwasit Ghulam in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)PermalinkA fully constrained linear spectral unmixing algorithm based on distance geometry / Hanye Pu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)PermalinkNonlinear unmixing of hyperspectral data using semi-nonnegative matrix factorization / Naoto Yokoya in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)PermalinkStructured sparse method for hyperspectral unmixing / Feiyun Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)PermalinkCollaborative sparse regression for hyperspectral unmixing / Marian-Daniel Iordache in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)PermalinkUnmixing polarimetric radar images based on land cover type before target decomposition / Sébastien Giordano (2014)PermalinkThe use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques / Chengbin Deng in ISPRS Journal of photogrammetry and remote sensing, vol 86 (December 2013)PermalinkMapping and assessing of urban impervious areas using multiple endmember spectral mixture analysis: a case study in the city of Tampa, Florida / Fenqing Weng in Geocarto international, vol 28 n° 7-8 (November - December 2013)PermalinkDeblurring and sparse unmixing for hyperspectral images / Xi-Le Zhao in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)PermalinkSpectral unmixing in multiple-kernel Hilbert space for hyperspectral imagery / Yanfeng Gu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)PermalinkManifold regularized sparse NMF for hyperspectral unmixing / Xiaqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)PermalinkPiecewise convex multiple-model endmember detection and spectral unmixing / Alina Zare in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)Permalink