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Individual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data / Sitinor Atikah Nordin in Geocarto international, vol 34 n° 11 ([15/08/2019])
[article]
Titre : Individual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data Type de document : Article/Communication Auteurs : Sitinor Atikah Nordin, Auteur ; Zulkiflee Abd Latif, Auteur ; Hamdan Omar, Auteur Année de publication : 2019 Article en page(s) : pp 1218 - 1236 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes principales
[Termes IGN] analyse multibande
[Termes IGN] Asie du sud-est
[Termes IGN] bande rouge
[Termes IGN] canopée
[Termes IGN] capteur hyperspectral
[Termes IGN] carte forestière
[Termes IGN] forêt tropicale
[Termes IGN] image hyperspectrale
[Termes IGN] image proche infrarouge
[Termes IGN] image satellite
[Termes IGN] niveau de gris (image)
[Termes IGN] réflectance végétale
[Termes IGN] segmentation d'image
[Termes IGN] teneur en chlorophylle des feuilles
[Termes IGN] tourbièreRésumé : (Auteur) Individual tree crown segmentation is important step for deriving various information for fine-scale analysis of ecological process. However, only several studies have applied tree crown segmentation in tropical forest ecosystems, especially in mixed peat swamp forests. In this study, hyperspectral data were used to detect changes in the biochemical and biophysical characteristics, which are important factors for tree crown segmentation. Principal Component Analysis method was performed to investigate its influence on crown segmentation. Visually Selected PCs, 160 PCs and 160 Spectral Bands image were used and two segmentation techniques; Watershed Transformation and Region Growing segmentation were applied on those images. The highest accuracy was achieved for the crown segmentation is using Region Growing segmentation, based on 1:1 measurement, D value and RMSE value. The results obtained from 160 PCs image using region growing algorithm shows better accuracy with D value of 0.2 (80% accuracy, 20% error) and RMSE of 9.9 m2. Numéro de notice : A2019-463 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1475511 Date de publication en ligne : 24/05/2018 En ligne : https://doi.org/10.1080/10106049.2018.1475511 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93605
in Geocarto international > vol 34 n° 11 [15/08/2019] . - pp 1218 - 1236[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2019111 RAB Revue Centre de documentation En réserve L003 Disponible Mapping the wavelength position of mineral features in hyperspectral thermal infrared data / Christoph Hecker in International journal of applied Earth observation and geoinformation, vol 79 (July 2019)
[article]
Titre : Mapping the wavelength position of mineral features in hyperspectral thermal infrared data Type de document : Article/Communication Auteurs : Christoph Hecker, Auteur ; Frank J.A. Van Ruitenbeek, Auteur ; Wim H. Bakker, Auteur ; Babatunde J. Fagbohun, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 133-140 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte géologique
[Termes IGN] feldspath
[Termes IGN] filtrage du bruit
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] image thermique
[Termes IGN] longueur d'onde
[Termes IGN] Nevada (Etats-Unis)
[Termes IGN] prospection minérale
[Termes IGN] quartzRésumé : (auteur) The Wavelength Mapper is an algorithm that searches for the deepest absorption feature in each pixel of a hyperspectral image. On a per pixel basis, it extracts the wavelength position, which serves as a proxy of the mineralogy and the feature depth as a proxy for the relative abundance. This algorithm has been used with near and shortwave infrared data, but has not yet been tested on hyperspectral thermal infrared images. It is unclear what results are expected when the Wavelength Mapper algorithm is applied to hyperspectral thermal infrared data since reststrahlen features characteristically overlap in emissivity spectra. In this paper, the Wavelength Mapper is tested on a multi-flightline airborne hyperspectral TIR dataset acquired over the Yerington Batholith, Nevada. Observations were made in the 8.05–11.65 μm wavelength range to include thermal spectral features of major rock-forming minerals, and a new color ramp is created to separate quartz-rich rocks from plagioclase-rich rocks. Our results indicate that the Wavelength Mapper creates coherent spatial patterns across flightlines. The results displayed represent different types of igneous and sedimentary rocks, as well as the products of hydrothermal alteration via different colors, mainly based on the relative abundance of quartz, feldspar and garnet, as well as mica and epidote. Comparison with published maps indicate that the Wavelength Mapper represents for each pixel a parameter value that can be linked to the spectrally dominate rock-forming mineral of that area, as mapped with traditional fieldwork methods. In conclusion, the Wavelength Mapper can be applied to airborne hyperspectral TIR data to achieve a simple, repeatable, per-pixel overview map of the dominating rock-forming mineral occurrences. Numéro de notice : A2019-467 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.02.013 Date de publication en ligne : 20/03/2019 En ligne : https://doi.org/10.1016/j.jag.2019.02.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93603
in International journal of applied Earth observation and geoinformation > vol 79 (July 2019) . - pp 133-140[article]3D 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)
[article]
Titre : 3D hyperspectral point cloud generation: Fusing airborne laser scanning and hyperspectral imaging sensors for improved object-based information extraction Type de document : Article/Communication Auteurs : Maximilian Brell, Auteur ; Karl Segl, Auteur ; Luis Guanter, Auteur ; Bodo Bookhagen, Auteur Année de publication : 2019 Article en page(s) : pp 200 - 214 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] capteur hyperspectral
[Termes IGN] classification orientée objet
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] fusion de données
[Termes IGN] image hyperspectrale
[Termes IGN] impulsion laser
[Termes IGN] niveau de détail
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (Auteur) Remote Sensing technologies allow to map biophysical, biochemical, and earth surface parameters of the land surface. Of especial interest for various applications in environmental and urban sciences is the combination of spectral and 3D elevation information. However, those two data streams are provided separately by different instruments, namely airborne laser scanner (ALS) for elevation and a hyperspectral imager (HSI) for high spectral resolution data. The fusion of ALS and HSI data can thus lead to a single data entity consistently featuring rich structural and spectral information. In this study, we present the application of fusing the first pulse return information from ALS data at a sub-decimeter spatial resolution with the lower-spatial resolution hyperspectral information available from the HSI into a hyperspectral point cloud (HSPC). During the processing, a plausible hyperspectral spectrum is assigned to every first-return ALS point. We show that the complementary implementation of spectral and 3D information at the point-cloud scale improves object-based classification and information extraction schemes. This improvements have great potential for numerous land-cover mapping and environmental applications. Numéro de notice : A2019-119 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.022 Date de publication en ligne : 06/02/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.022 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92448
in ISPRS Journal of photogrammetry and remote sensing > vol 149 (March 2019) . - pp 200 - 214[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019031 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Conditional random field and deep feature learning for hyperspectral image classification / Fahim Irfan Alam in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)
[article]
Titre : Conditional random field and deep feature learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Fahim Irfan Alam, Auteur ; Jun Zhou, Auteur ; Alan Wee-Chung Liew, Auteur ; Xiuping Jia, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1612 - 1628 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse multibande
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] déconvolution
[Termes IGN] données localisées 3D
[Termes IGN] image hyperspectrale
[Termes IGN] voxelRésumé : (Auteur) Image classification is considered to be one of the critical tasks in hyperspectral remote sensing image processing. Recently, a convolutional neural network (CNN) has established itself as a powerful model in classification by demonstrating excellent performances. The use of a graphical model such as a conditional random field (CRF) contributes further in capturing contextual information and thus improving the classification performance. In this paper, we propose a method to classify hyperspectral images by considering both spectral and spatial information via a combined framework consisting of CNN and CRF. We use multiple spectral band groups to learn deep features using CNN, and then formulate deep CRF with CNN-based unary and pairwise potential functions to effectively extract the semantic correlations between patches consisting of 3-D data cubes. Furthermore, we introduce a deep deconvolution network that improves the final classification performance. We also introduced a new data set and experimented our proposed method on it along with several widely adopted benchmark data sets to evaluate the effectiveness of our method. By comparing our results with those from several state-of-the-art models, we show the promising potential of our method. Numéro de notice : A2019-131 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2867679 Date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2867679 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92461
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 3 (March 2019) . - pp 1612 - 1628[article]Hyperspectral image classification with squeeze multibias network / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)
[article]
Titre : Hyperspectral image classification with squeeze multibias network Type de document : Article/Communication Auteurs : Leyuan Fang, Auteur ; Guangyun Liu, Auteur ; Shutao Li, Auteur ; Pedram Ghamisi, Auteur ; Jon Atli Benediktsson, Auteur Année de publication : 2019 Article en page(s) : pp 1291 - 1301 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] erreur systématique
[Termes IGN] image hyperspectraleRésumé : (Auteur) A convolutional neural network (CNN) has recently demonstrated its outstanding capability for the classification of hyperspectral images (HSIs). Typical CNN-based methods usually adopt image patches as inputs to the network. However, a fixed-size image patch in HSI with complex spatial contexts may contain multiple ground objects of different classes, which will deteriorate the classification performance of the CNN. In addition, traditional convolutional layers adopted in the CNN have a huge amount of parameters needed to be tuned, which will cause high computational cost. To address the above-mentioned issues, a novel squeeze multibias network (SMBN) is proposed for HSI classification. Specifically, the proposed SMBN first introduces the multibias module (MBM), which incorporates multibias into the rectified linear unit layers. The MBM can decouple the feature maps of input patches into multiple response maps (corresponding to different ground objects) and adaptively select the meaningful maps for classification. Furthermore, the proposed SMBN replaces the traditional convolutional layer with a squeeze convolution module, which can greatly reduce the number of parameters in the network, thus saving the running time, while still maintaining high classification accuracy. Experimental results on three real HSIs demonstrate the superiority of the proposed SMBN method over several state-of-the-art classification approaches. Numéro de notice : A2019-113 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2865953 Date de publication en ligne : 13/09/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2865953 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92453
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 3 (March 2019) . - pp 1291 - 1301[article]PermalinkEvaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands / Fabio Castaldi in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkExploitation of hyperspectral data for assessing vegetation health under exposure to petroleum hydrocarbons / Guillaume Lassalle (2019)PermalinkGeographic Information Systems in Geospatial Intelligence, ch. 5. Spectral optimization of airborne multispectral camera for land cover classification: automatic feature selection and spectral band clustering / Arnaud Le Bris (2019)PermalinkPermalinkPermalinkPermalinkMacroalgues intertidales : Apport de la télédétection hyperspectrale pour le suivi sectoriel dans le cadre de la DCE/DCSMM / Arnaud Le Bris (2019)PermalinkSensitivity of urban material classification to spatial and spectral configurations from visible to short-wave infrared / Arnaud Le Bris (2019)PermalinkSpectral unmixing with perturbed endmembers / Reza Arablouei in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)Permalink