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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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 149 (March 2019) . - pp 200 - 214 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 descripteurs IGN] analyse des mélanges spectraux [Termes descripteurs IGN] capteur hyperspectral [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] étiquetage sémantique [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] impulsion laser [Termes descripteurs IGN] niveau de détail [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] semis de points Résumé : (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 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 [article]

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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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 3 (March 2019) . - pp 1612 - 1628 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 descripteurs IGN] analyse multibande [Termes descripteurs IGN] champ aléatoire conditionnel [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] déconvolution [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] voxel Résumé : (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 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 [article]

Hyperspectral image classification with squeeze multibias network / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 3 (March 2019) . - pp 1291 - 1301 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 descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] erreur systématique [Termes descripteurs IGN] image hyperspectrale Résumé : (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 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 [article]

Evaluating 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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 147 (January 2019) . - pp 267 - 282 Titre : Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands Type de document : Article/Communication Auteurs : Fabio Castaldi, Auteur ; Andreas Hueni, Auteur ; Sabine Chabrillat, Auteur ; Kathrin Ward, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 267 - 282 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Allemagne [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] analyse multivariée [Termes descripteurs IGN] Belgique [Termes descripteurs IGN] capacité de stockage [Termes descripteurs IGN] carbone [Termes descripteurs IGN] image APEX [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] Luxembourg [Termes descripteurs IGN] rapport signal sur bruit [Termes descripteurs IGN] sol [Termes descripteurs IGN] surface cultivée Résumé : (auteur) The short revisit time of the Sentinel-2 (S2) constellation entails a large availability of remote sensing data, but S2 data have been rarely used to predict soil organic carbon (SOC) content. Thus, this study aims at comparing the capability of multispectral S2 and airborne hyperspectral remote sensing data for SOC prediction, and at the same time, we investigated the importance of spectral and spatial resolution through the signal-to-noise ratio (SNR), the variable importance in the prediction (VIP) models and the spatial variability of the SOC maps at field and regional scales. We tested the capability of the S2 data to predict SOC in croplands with quite different soil types and parent materials in Germany, Luxembourg and Belgium, using multivariate statistics and local ground calibration with soil samples. We split the calibration dataset into sub-regions according to soil maps and built a multivariate regression model within each sub-region. The prediction accuracy obtained by S2 data is generally slightly lower than that retrieved by airborne hyperspectral data. The ratio of performance to deviation (RPD) is higher than 2 in Luxembourg (2.6) and German (2.2) site, while it is 1.1 in the Belgian area. After the spectral resampling of the airborne data according to S2 band, the prediction accuracy did not change for four out of five of the sub-regions. The variable importance values obtained by S2 data showed the same trend as the airborne VIP values, while the importance of SWIR bands decreased using airborne data resampled according the S2 bands. These differences of VIP values can be explained by the loss of spectral resolution as compared to APEX data and the strong difference in terms of SNR between the SWIR region and other spectral regions. The investigation on the spatial variability of the SOC maps derived by S2 data has shown that the spatial resolution of S2 is adequate to describe SOC variability both within field and at regional scale. Numéro de notice : A2019-037 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.11.026 date de publication en ligne : 06/12/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.11.026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91974 [article]

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Sensitivity of urban material classification to spatial and spectral configurations from visible to short-wave infrared / Arnaud Le Bris (2019)    

Public contenu dans JURSE 2019, Joint Urban Remote Sensing Event / T. Corpetti (2019)   Titre : Sensitivity of urban material classification to spatial and spectral configurations from visible to short-wave infrared Type de document : Article/Communication Auteurs : Arnaud Le Bris , Auteur ; Nesrine Chehata , Auteur Congrès : JURSE 2019, Joint Urban Remote Sensing Event (22 - 24 mai 2019; Vannes, France) , Commanditaire Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN Année de publication : 2019 Autre Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Projets : HYEP / Weber, Christiane Importance : 4 p. Format : 21 x 30 cm Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] analyse de sensibilité [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] objet géographique urbain [Termes descripteurs IGN] pouvoir de résolution géométrique [Termes descripteurs IGN] signature spectrale Résumé : (Auteur) Urban material maps are useful for several city modeling or monitoring applications and can be retrieved from remote sensing data. This study investigates the impact of spectral and spatial sensor configuration on urban material classification results, comparing several configurations corresponding to existing or envisaged airborne or space sensors. Images corresponding to such sensors were simulated out of an airborne hyperspectral acquisition. At the end, the relevance of an enhanced spectral configuration and especially providing bands from the SWIR domain was proven, as well as the need for a fine spatial resolution to retrieve urban objects. However, the (late) fusion of multispectral imagery at 2 m resolution with hyperspectral data at 8 m resolution was also proven to lead to good results. Numéro de notice : C2019-005 Thématique : IMAGERIE/URBANISME Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/JURSE.2019.8809029 date de publication en ligne : 22/08/2019 En ligne : https://doi.org/10.1109/JURSE.2019.8809029 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92587

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Spectral unmixing with perturbed endmembers / Reza Arablouei in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)  

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Detection of individual trees in urban alignment from airborne data and contextual information: A marked point process approach / Josselin Aval in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)  

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A new deep convolutional neural network for fast hyperspectral image classification / Mercedes Eugenia Paoletti in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)  

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3-D deep learning approach for remote sensing image classification / Amina Ben Hamida in IEEE Transactions on geoscience and remote sensing, vol 56 n° 8 (August 2018)  

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Detecting newly grown tree leaves from unmanned-aerial-vehicle images using hyperspectral target detection techniques / Chinsu Lin in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

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ICARE-VEG: A 3D physics-based atmospheric correction method for tree shadows in urban areas / Karine R.M. Adeline in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

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Spectral-spatial classification of hyperspectral images using wavelet transform and hidden Markov random fields / Elham Kordi Ghasrodashti in Geocarto international, vol 33 n° 8 (August 2018)  

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Evolutionary approach for detection of buried remains using hyperspectral images / Leon Dozal in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 7 (juillet 2018)  

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Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform / Mohd Shahrimie Mohd Asaari in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)  

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Sensitivity analysis of pansharpening in hyperspectral change detection / Seyd Teymoor Seydi in Applied geomatics, vol 10 n° 1 (March 2018)  

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