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Retrieval of leaf area index in different plant species using thermal hyperspectral data / Elnaz Neinavaz in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
[article]
Titre : Retrieval of leaf area index in different plant species using thermal hyperspectral data Type de document : Article/Communication Auteurs : Elnaz Neinavaz, Auteur ; Andrew K. Skidmore, Auteur ; Roshanak Darvishzadeh, Auteur ; Thomas A. Groen, Auteur Année de publication : 2016 Article en page(s) : pp 390 - 401 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Buxus sempervirens
[Termes IGN] classification par réseau neuronal
[Termes IGN] espèce végétale
[Termes IGN] Euonymus japonicus
[Termes IGN] image hyperspectrale
[Termes IGN] indice foliaire
[Termes IGN] Leaf Area Index
[Termes IGN] méthode des moindres carrés
[Termes IGN] photo-interprétation
[Termes IGN] rayonnement infrarouge thermique
[Termes IGN] régression
[Termes IGN] Rhododendron (genre)Résumé : (Auteur) Leaf area index (LAI) is an important variable of terrestrial ecosystems because it is strongly correlated with many ecosystem processes (e.g., water balance and evapotranspiration) and directly related to the plant energy balance and gas exchanges. Although LAI has been accurately predicted using visible and short-wave infrared hyperspectral data (0.3–2.5 μm), LAI estimation using thermal infrared (TIR, 8–14 μm) measurements has not yet been addressed. The novel approach of this study is to evaluate the retrieval of LAI using TIR hyperspectral data. The leaf area indices were destructively acquired for four plant species: Azalea japonica, Buxus sempervirens, Euonymus japonicus, and Ficus benjamina. Canopy emissivity spectral measurements were obtained under controlled laboratory conditions using a MIDAC (M4401-F) spectrometer. The LAI retrieval was assessed using a partial least squares regression (PLSR), artificial neural networks (ANNs), and narrow band indices calculated from all possible combinations of waveband pairs for three vegetation indices including simple difference, simple ratio, and normalized difference. ANNs retrieved LAI more accurately than PLSR and vegetation indices (0.67 Numéro de notice : A2016-789 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.07.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.07.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82505
in ISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 390 - 401[article]Semiblind 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)
[article]
Titre : Semiblind hyperspectral unmixing in the presence of spectral library mismatches Type de document : Article/Communication Auteurs : Xiao Fu, Auteur ; Wing-Kin Ma, Auteur ; José M. Bioucas-Dias, Auteur ; Tsung-Han Chan, Auteur Année de publication : 2016 Article en page(s) : pp 5171 - 5184 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] données clairsemées
[Termes IGN] image hyperspectrale
[Termes IGN] itération
[Termes IGN] régressionRésumé : (Auteur) The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing in remote sensing. By using an available spectral library as a dictionary, the SR approach identifies the underlying materials in a given hyperspectral image by selecting a small subset of spectral samples in the dictionary to represent the whole image. A drawback with the current SR developments is that an actual spectral signature in the scene is often assumed to have zero mismatch with its corresponding dictionary sample, and such an assumption is considered too ideal in practice. In this paper, we tackle the spectral signature mismatch problem by proposing a dictionary-adjusted nonconvex sparsity-encouraging regression (DANSER) framework. The main idea is to incorporate dictionary-correcting variables in an SR formulation. A simple and low per-iteration complexity algorithm is tailor-designed for practical realization of DANSER. Using the same dictionary-correcting idea, we also propose a robust subspace solution for dictionary pruning. Extensive simulations and real-data experiments show that the proposed method is effective in mitigating the undesirable spectral signature mismatch effects. Numéro de notice : A2016-896 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2557340 En ligne : https://doi.org/10.1109/TGRS.2016.2557340 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83087
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 9 (September 2016) . - pp 5171 - 5184[article]The impact of integrating WorldView-2 sensor and environmental variables in estimating plantation forest species aboveground biomass and carbon stocks in uMgeni Catchment, South Africa / Timothy Dube in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
[article]
Titre : The impact of integrating WorldView-2 sensor and environmental variables in estimating plantation forest species aboveground biomass and carbon stocks in uMgeni Catchment, South Africa Type de document : Article/Communication Auteurs : Timothy Dube, Auteur ; Onisimo Mutanga, Auteur Année de publication : 2016 Article en page(s) : pp 415 - 425 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Afrique du sud (état)
[Termes IGN] biomasse
[Termes IGN] biomasse aérienne
[Termes IGN] écosystème forestier
[Termes IGN] Eucalyptus dunii
[Termes IGN] Eucalyptus grandis
[Termes IGN] image multibande
[Termes IGN] image Worldview
[Termes IGN] Pinus taeda
[Termes IGN] puits de carbone
[Termes IGN] teneur en carboneRésumé : (Auteur) Reliable and accurate mapping and extraction of key forest indicators of ecosystem development and health, such as aboveground biomass (AGB) and aboveground carbon stocks (AGCS) is critical in understanding forests contribution to the local, regional and global carbon cycle. This information is critical in assessing forest contribution towards ecosystem functioning and services, as well as their conservation status. This work aimed at assessing the applicability of the high resolution 8-band WorldView-2 multispectral dataset together with environmental variables in quantifying AGB and aboveground carbon stocks for three forest plantation species i.e. Eucalyptus dunii (ED), Eucalyptus grandis (EG) and Pinus taeda (PT) in uMgeni Catchment, South Africa. Specifically, the strength of the Worldview-2 sensor in terms of its improved imaging agilities is examined as an independent dataset and in conjunction with selected environmental variables. The results have demonstrated that the integration of high resolution 8-band Worldview-2 multispectral data with environmental variables provide improved AGB and AGCS estimates, when compared to the use of spectral data as an independent dataset. The use of integrated datasets yielded a high R2 value of 0.88 and RMSEs of 10.05 t ha−1 and 5.03 t C ha−1 for E. dunii AGB and carbon stocks; whereas the use of spectral data as an independent dataset yielded slightly weaker results, producing an R2 value of 0.73 and an RMSE of 18.57 t ha−1 and 09.29 t C ha−1. Similarly, high accurate results (R2 value of 0.73 and RMSE values of 27.30 t ha−1 and 13.65 t C ha−1) were observed from the estimation of inter-species AGB and carbon stocks. Overall, the findings of this work have shown that the integration of new generation multispectral datasets with environmental variables provide a robust toolset required for the accurate and reliable retrieval of forest aboveground biomass and carbon stocks in densely forested terrestrial ecosystems. Numéro de notice : A2016-790 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.06.017 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.06.017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82506
in ISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 415 - 425[article]Two heads are better than one / Brian Curtiss in GEO: Geoconnexion international, vol 15 n° 8 (September 2016)
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Titre : Two heads are better than one Type de document : Article/Communication Auteurs : Brian Curtiss, Auteur Année de publication : 2016 Article en page(s) : pp 33 - 37 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement automatique
[Termes IGN] appariement d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] spectroradiométrieRésumé : (éditeur) Using linked spectroradiometers enables far better collection of field reflectance spectra, to improve the matching of remote sensed imagery.Brian Curtiss shows how it can be done Numéro de notice : A2016-662 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81893
in GEO: Geoconnexion international > vol 15 n° 8 (September 2016) . - pp 33 - 37[article]Dirichlet process based active learning and discovery of unknown classes for hyperspectral image classification / Hao Wu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)
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Titre : Dirichlet process based active learning and discovery of unknown classes for hyperspectral image classification Type de document : Article/Communication Auteurs : Hao Wu, Auteur ; Saurabh Prasad, Auteur Année de publication : 2016 Article en page(s) : pp 4882 - 4895 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification
[Termes IGN] image hyperspectrale
[Termes IGN] problème de DirichletRésumé : (Auteur) Active learning is an area of significant ongoing research interest for the classification of remotely sensed data, where obtaining efficient training data is both time consuming and expensive. The goal of active learning is to achieve high classification performance by querying as few samples as possible from a large unlabeled data pool. Traditional active learning frameworks all assume the existence of labeled samples for all classes of interest. However, in real-world applications, the unlabeled data pool may contain data from unknown classes that we are not aware of in advance, and a quick detection of them is useful for enriching our training set. In this scenario, traditional active learning methods may not effectively and rapidly detect the unknown classes. We proposed an active learning framework which provides robust classification performance with minimum manual labeling effort while simultaneously discovering unknown (missing) classes. The discovery of unknown classes is particularly suited to an active learning framework where an annotator is in the loop. A Dirichlet process mixture model is utilized in our proposed method to cluster the labeled and unlabeled samples as a whole. If unknown classes exist, they will emerge as new clusters which are different from other existing clusters occupied by known classes, and then, the proposed query strategy will give priority to querying samples in the new clusters. We present experimental results with hyperspectral data to show that our method provides better classification performance compared to existing active learning methods with or without unknown classes. Numéro de notice : A2016-892 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2552507 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2552507 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83072
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 8 (August 2016) . - pp 4882 - 4895[article]Simultaneously 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)PermalinkEfficient multiple-feature learning-based hyperspectral image classification with limited training samples / Chongyue Zhao in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkEstimating the intrinsic dimension of hyperspectral images using a noise-whitened eigengap approach / Abderrahim Halimi in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkFusion of LiDAR orthowaveforms and hyperspectral imagery for shallow river bathymetry and turbidity estimation / Zhigang Pan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkMultiple spectral similarity metrics for surface materials identification using hyperspectral data / Rama Rao Nidamanuri in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)PermalinkRecursive orthogonal projection-based simplex growing algorithm / Hsiao-Chi Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkSparse and low-rank graph for discriminant analysis of hyperspectral imagery / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 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)PermalinkSpectral band selection for urban material classification using hyperspectral libraries / Arnaud Le Bris in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-7 (July 2016)PermalinkFusion of hyperspectral and VHR multispectral image classifications in urban α–areas / Alexandre Hervieu in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-3 (July 2016)Permalink