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Unsupervised 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)
[article]
Titre : Unsupervised multitemporal spectral unmixing for detecting multiple changes in hyperspectral images Type de document : Article/Communication Auteurs : Sicong Liu, Auteur ; Lorenzo Bruzzone, Auteur ; Francesca Bovolo, Auteur ; Peijun Du, Auteur Année de publication : 2016 Article en page(s) : pp 2733 - 2748 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse des mélanges spectraux
[Termes IGN] analyse infrapixellaire
[Termes IGN] détection de changement
[Termes IGN] image hyperspectrale
[Termes IGN] image multitemporelleRésumé : (Auteur) This paper presents a novel multitemporal spectral unmixing (MSU) approach to address the challenging multiple-change detection problem in bitemporal hyperspectral (HS) images. Differently from the state-of-the-art methods that are mainly designed at a pixel level, the proposed technique investigates the spectral-temporal variations at a subpixel level. The considered change detection (CD) problem is analyzed in a multitemporal domain, where a bitemporal spectral mixture model is defined to analyze the spectral composition within a pixel. Distinct multitemporal endmembers (MT-EMs) are extracted according to an automatic and unsupervised technique. Then, a change analysis strategy is designed to distinguish the change and no-change MT-EMs. An endmember-grouping scheme is applied to the changed MT-EMs to detect the unique change classes. Finally, the considered multiple-change detection problem is solved by analyzing the abundances of the change and no-change classes and their contribution to each pixel. The proposed approach has been validated on both simulated and real multitemporal HS data sets presenting multiple changes. Experimental results confirmed the effectiveness of the proposed method. Numéro de notice : A2016-846 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2505183 En ligne : https://doi.org/10.1109/TGRS.2015.2505183 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82927
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 5 (May 2016) . - pp 2733 - 2748[article]Forest above ground biomass inversion by fusing GLAS with optical remote sensing data / Xiaohuan Xi in ISPRS International journal of geo-information, vol 5 n° 4 (April 2016)
[article]
Titre : Forest above ground biomass inversion by fusing GLAS with optical remote sensing data Type de document : Article/Communication Auteurs : Xiaohuan Xi, Auteur ; Tingting Han, Auteur ; Cheng Wang, Auteur ; et al., Auteur Année de publication : 2016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] classification par réseau neuronal
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] données ICEsat
[Termes IGN] forêt
[Termes IGN] hauteur de la végétation
[Termes IGN] image Landsat-TM
[Termes IGN] image optique
[Termes IGN] image Terra-MODIS
[Termes IGN] Leaf Area Index
[Termes IGN] MNS ASTER
[Termes IGN] régression
[Termes IGN] Yunnan (Chine)Résumé : (auteur) Forest biomass is an important parameter for quantifying and understanding biological and physical processes on the Earth’s surface. Rapid, reliable, and objective estimations of forest biomass are essential to terrestrial ecosystem research. The Geoscience Laser Altimeter System (GLAS) produced substantial scientific data for detecting the vegetation structure at the footprint level. This study combined GLAS data with MODIS/BRDF (Bidirectional Reflectance Distribution Function) and ASTER GDEM data to estimate forest aboveground biomass (AGB) in Xishuangbanna, Yunnan Province, China. The GLAS waveform characteristic parameters were extracted using the wavelet method. The ASTER DEM was used to compute the terrain index for reducing the topographic influence on the GLAS canopy height estimation. A neural network method was applied to assimilate the MODIS BRDF data with the canopy heights for estimating continuous forest heights. Forest leaf area indices (LAIs) were derived from Landsat TM imagery. A series of biomass estimation models were developed and validated using regression analyses between field-estimated biomass, canopy height, and LAI. The GLAS-derived canopy heights in Xishuangbanna correlated well with the field-estimated AGB (R2 = 0.61, RMSE = 52.79 Mg/ha). Combining the GLAS estimated canopy heights and LAI yielded a stronger correlation with the field-estimated AGB (R2 = 0.73, RMSE = 38.20 Mg/ha), which indicates that the accuracy of the estimated biomass in complex terrains can be improved significantly by integrating GLAS and optical remote sensing data. Numéro de notice : A2016-820 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi5040045 En ligne : https://doi.org/10.3390/ijgi5040045 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82625
in ISPRS International journal of geo-information > vol 5 n° 4 (April 2016)[article]Comparative 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)
[article]
Titre : Comparative 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 Type de document : Article/Communication Auteurs : Arindam Guha, Auteur ; K. Vinod Kumar, Auteur Année de publication : 2016 Article en page(s) : pp 367 - 384 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] aluminium
[Termes IGN] analyse comparative
[Termes IGN] analyse discriminante
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] image hyperspectrale
[Termes IGN] image Terra-ASTER
[Termes IGN] Inde
[Termes IGN] mineraiRésumé : (Auteur) We have attempted comparative analysis of the utility of linear spectral unmixing (LSU) method and band ratios for delineating bauxite from laterite within the lateritic bauxite provinces of Chotonagpur Plateau, Jharkhand of India. This was attempted based on processing of visible–near infrared (VNIR) and shortwave infrared (SWIR) spectral bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. In LSU method, spectral features of main constituent minerals of lateritic bauxite are used to decompose the pixel spectra to estimate the relative abundance of bauxite and laterite in each pixel to spatially delineate bauxite within laterite. We have also compared the bauxite map derived using LSU method with bauxite maps of two band ratios in terms of spatial disposition of bauxite. We also have attempted to relate the abundance values of pixels of LSU-based bauxite map with band ratio values of bauxite pixels of two selected bauxite indices. Numéro de notice : A2016-155 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1047471 Date de publication en ligne : 10/06/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1047471 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80395
in Geocarto international > vol 31 n° 3 - 4 (March - April 2016) . - pp 367 - 384[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2016021 RAB Revue Centre de documentation En réserve L003 Disponible Comparative study on projected clustering methods for hyperspectral imagery classification / Anand Mehta in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)
[article]
Titre : Comparative study on projected clustering methods for hyperspectral imagery classification Type de document : Article/Communication Auteurs : Anand Mehta, Auteur ; Onkar Dikshit, Auteur Année de publication : 2016 Article en page(s) : pp 296 - 307 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] classification semi-dirigée
[Termes IGN] image hyperspectrale
[Termes IGN] OrclusRésumé : (Auteur) In this study, projected clustering is introduced to hyperspectral imagery for unsupervised classification. The main advantage of projected clustering lies in its ability to simultaneously perform feature selection and clustering. This framework also allows selection of different sets of dimensions (features/bands) for different clusters. This framework provides an effective way to address the issues associated with the high dimensionality of the data. Experiments are conducted on both synthetic and real hyperspectral imagery. For this purpose, projected clustering algorithms are implemented and compared with k-means and k-means preceded by principal component analysis. Preliminary analyses of studied algorithms on synthetic hyperspectral imagery demonstrate good results. For real hyperspectral imagery, only ORCLUS is able to produce acceptable results as compared to other unsupervised methods. The main concern lies with identification of right parameter settings. More experiments are required in this direction. Numéro de notice : A2016-152 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1047416 Date de publication en ligne : 26/05/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1047416 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80385
in Geocarto international > vol 31 n° 3 - 4 (March - April 2016) . - pp 296 - 307[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2016021 RAB Revue Centre de documentation En réserve L003 Disponible Noise simulation and correction in synthetic airborne TIR Data for mineral quantification / Christoph Hecker in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)
[article]
Titre : Noise simulation and correction in synthetic airborne TIR Data for mineral quantification Type de document : Article/Communication Auteurs : Christoph Hecker, Auteur ; Dean Riley, Auteur ; Mark Van Der Meijde, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 1545 - 1553 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] erreur systématique
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] image thermique
[Termes IGN] prospection minérale
[Termes IGN] quartz
[Termes IGN] rapport signal sur bruit
[Termes IGN] rayonnement infrarouge thermique
[Termes IGN] régression
[Termes IGN] simulationRésumé : (Auteur) Rock-forming minerals (such as feldspar and quartz) can be identified and quantified from thermal infrared (TIR) laboratory spectroscopy using spectral models. This paper uses synthetic airborne TIR spectra to test whether the hyperspectral Spatially Enhanced Broadband Array Spectrograph System (SEBASS) would theoretically be able to detect quartz and feldspar minerals and quantitatively predict mineral modes in felsic igneous rocks. Data from a previous laboratory study were used to simulate TIR spectra with band locations and noise levels of the SEBASS sensor. The quantitative partial least squares regression (PLSR) models from that study were applied to newly created synthetic SEBASS data, and results were compared with the predictions from the previous study. Predicted compositions based on SEBASS band positions are nearly identical (ρ = 0.995) to those based on laboratory resolution. Results are still reliable [prediction errors within 0.4% (absolute)] to the original laboratory PLSR predictions when adding up to 1% noise (about five times the SEBASS noise level) to the synthetic data. Prediction errors rapidly increase when noise levels beyond 1% are used. These results show that SEBASS' spectral resolution, spectral coverage, and signal-to-noise levels are sufficient to quantitatively predict quartz and feldspar amounts, and feldspar compositions with models based on PLSR. Spectral distortions, such as reduced spectral contrast, tilts, and vertical shifts, must be compensated for before these quantitative models are applied. A mean and standard deviation (MASD) normalization is proposed using a set of ground data for compensating systematic errors that are common to all image pixels. Numéro de notice : A2016-124 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2482386 En ligne : https://doi.org/10.1109/TGRS.2015.2482386 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80005
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 3 (March 2016) . - pp 1545 - 1553[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2016031 SL Revue Centre de documentation Revues en salle Disponible A penalized spline-based attitude model for high-resolution satellite imagery / Hongbo Pan 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)PermalinkMatrix-based discriminant subspace ensemble for hyperspectral image spatial–spectral feature fusion / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)PermalinkTelespazio aurait-il trouvé la solution pour développer l'usage du spatial / Françoise de Blomac in DécryptaGéo le mag, n° 174 (février 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)PermalinkContributions à la segmentation non supervisée d'images hyperspectrales : trois approches algébriques et géométriques / Saadallah El Asmar (2016)PermalinkPermalinkForest stand segmentation using airborne lidar data and very high resolution multispectral imagery / Clément Dechesne (2016)PermalinkFusion of hyperspectral images and digital surface models for urban object extraction / Janja Avbelj (2016)PermalinkInverse procedural Street Modelling: from interactive to automatic reconstruction / Rémi Cura (2016)PermalinkMultifractal analysis for multivariate data with application to remote sensing / Sébastien Combrexelle (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)PermalinkObject-oriented semantic labelling of spectral–spatial LiDAR point cloud for urban land cover classification and buildings detection / Anandakumar M. Ramiya in Geocarto international, vol 31 n° 1 - 2 (January - February 2016)PermalinkPointwise approach for texture analysis and characterization from very high resolution remote sensing images / Minh-Tan Pham (2016)PermalinkPermalinkSpectral–spatial adaptive sparse representation for hyperspectral image denoising / Ting Lu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)PermalinkTotal-variation-regularized low-rank matrix factorization for hyperspectral image restoration / Wei He in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)PermalinkPermalinkAn approach to fine coregistration between very high resolution multispectral images based on registration noise distribution / Youkyung Han in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)PermalinkClassification of hyperspectral images by exploiting spectral–spatial information of superpixel via multiple kernels / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)Permalink