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Hyperspectral and lidar intensity data fusion : A framework for the rigorous correction of illumination, anisotropic effects, and cross calibration / Maximilian Brell in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)
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Titre : Hyperspectral and lidar intensity data fusion : A framework for the rigorous correction of illumination, anisotropic effects, and cross calibration Type de document : Article/Communication Auteurs : Maximilian Brell, Auteur ; Karl Segl, Auteur ; Luis Guanter, Auteur ; Bodo Bookhagen, Auteur Année de publication : 2017 Article en page(s) : pp 2799 - 2810 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] anisotropie
[Termes IGN] correction radiométrique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] étalonnage croisé
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] intensité lumineuse
[Termes IGN] réflectance spectraleRésumé : (Auteur) The fusion of hyperspectral imaging (HSI) sensor and airborne lidar scanner (ALS) data provides promising potential for applications in environmental sciences. Standard fusion approaches use reflectance information from the HSI and distance measurements from the ALS to increase data dimensionality and geometric accuracy. However, the potential for data fusion based on the respective intensity information of the complementary active and passive sensor systems is high and not yet fully exploited. Here, an approach for the rigorous illumination correction of HSI data, based on the radiometric cross-calibrated return intensity information of ALS data, is presented. The cross calibration utilizes a ray tracing-based fusion of both sensor measurements by intersecting their particular beam shapes. The developed method is capable of compensating for the drawbacks of passive HSI systems, such as cast and cloud shadowing effects, illumination changes over time, across track illumination, and partly anisotropy effects. During processing, spatial and temporal differences in illumination patterns are detected and corrected over the entire HSI wavelength domain. The improvement in the classification accuracy of urban and vegetation surfaces demonstrates the benefit and potential of the proposed HSI illumination correction. The presented approach is the first step toward the rigorous in-flight fusion of passive and active system characteristics, enabling new capabilities for a variety of applications. Numéro de notice : A2017-469 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2654516 En ligne : https://doi.org/10.1109/TGRS.2017.2654516 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86392
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 5 (May 2017) . - pp 2799 - 2810[article]Self-taught feature learning for hyperspectral image classification / Ronald Kemker in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)
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Titre : Self-taught feature learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Ronald Kemker, Auteur ; Christopher Kanan, Auteur Année de publication : 2017 Article en page(s) : pp 2693 - 2705 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes indépendantes
[Termes IGN] apprentissage automatique
[Termes IGN] classification dirigée
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtrage numérique d'image
[Termes IGN] image hyperspectraleRésumé : (Auteur) In this paper, we study self-taught learning for hyperspectral image (HSI) classification. Supervised deep learning methods are currently state of the art for many machine learning problems, but these methods require large quantities of labeled data to be effective. Unfortunately, existing labeled HSI benchmarks are too small to directly train a deep supervised network. Alternatively, we used self-taught learning, which is an unsupervised method to learn feature extracting frameworks from unlabeled hyperspectral imagery. These models learn how to extract generalizable features by training on sufficiently large quantities of unlabeled data that are distinct from the target data set. Once trained, these models can extract features from smaller labeled target data sets. We studied two self-taught learning frameworks for HSI classification. The first is a shallow approach that uses independent component analysis and the second is a three-layer stacked convolutional autoencoder. Our models are applied to the Indian Pines, Salinas Valley, and Pavia University data sets, which were captured by two separate sensors at different altitudes. Despite large variation in scene type, our algorithms achieve state-of-the-art results across all the three data sets. Numéro de notice : A2017-467 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2651639 En ligne : https://doi.org/10.1109/TGRS.2017.2651639 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86390
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 5 (May 2017) . - pp 2693 - 2705[article]Superpixel-based multitask learning framework for hyperspectral image classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)
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Titre : Superpixel-based multitask learning framework for hyperspectral image classification Type de document : Article/Communication Auteurs : Sen Jia, Auteur ; Bin Deng, Auteur ; Jiasong Zhu, Auteur ; Xiuping Jia, Auteur Année de publication : 2017 Article en page(s) : pp 2575 - 2588 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] filtre de Gabor
[Termes IGN] image hyperspectraleRésumé : (Auteur) Due to the high spectral dimensionality of hyperspectral images as well as the difficult and time-consuming process of collecting sufficient labeled samples in practice, the small sample size scenario is one crucial problem and a challenging issue for hyperspectral image classification. Fortunately, the structure information of materials, reflecting region of homogeneity in the spatial domain, offers an invaluable complement to the spectral information. Assuming some spatial regularity and locality of surface materials, it is reasonable to segment the image into different homogeneous parts in advance, called superpixel, which can be used to improve the classification performance. In this paper, a superpixel-based multitask learning framework has been proposed for hyperspectral image classification. Specifically, a set of 2-D Gabor filters are first applied to hyperspectral images to extract discriminative features. Meanwhile, a superpixel map is generated from the hyperspectral images. Second, a superpixel-based spatial-spectral Schroedinger eigenmaps (S4E) method is adopted to effectively reduce the dimensions of each extracted Gabor cube. Finally, the classification is carried out by a support vector machine (SVM)-based multitask learning framework. The proposed approach is thus termed Gabor S4E and SVM-based multitask learning (GS4E-MTLSVM). A series of experiments is conducted on three real hyperspectral image data sets to demonstrate the effectiveness of the proposed GS4E-MTLSVM approach. The experimental results show that the performance of the proposed GS4E-MTLSVM is better than those of several state-of-the-art methods, while the computational complexity has been greatly reduced, compared with the pixel-based spatial-spectral Schroedinger eigenmaps method. Numéro de notice : A2017-466 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2647815 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2647815 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86389
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 5 (May 2017) . - pp 2575 - 2588[article]Urban land use/land cover discrimination using image-based reflectance calibration methods for hyperspectral data / Shailesh S. Deshpande in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 5 (May 2017)
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Titre : Urban land use/land cover discrimination using image-based reflectance calibration methods for hyperspectral data Type de document : Article/Communication Auteurs : Shailesh S. Deshpande, Auteur ; Arun B. Inamdar, Auteur ; Harrick M. Vin, Auteur Année de publication : 2017 Article en page(s) : pp 365 - 376 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse discriminante
[Termes IGN] étalonnage de capteur (imagerie)
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] Inde
[Termes IGN] occupation du sol
[Termes IGN] réflectance végétale
[Termes IGN] surface imperméable
[Termes IGN] surveillance de l'urbanisation
[Termes IGN] utilisation du sol
[Termes IGN] zone urbaineRésumé : (auteur) Irrespective of substantial research in land use/land cover (LULC) monitoring of urban area, hyperspectral data is not yet exploited effectively because of lack of local spectral resources and a practical reflectance calibration method. The objective of this research is to develop an effective methodology for urban LULC classification using image-based reflectance calibration methods: especially Vegetation-Impervious-Soil classes (VIS), using hyperspectral data. We used EO-1 Hyperion image of Pune City, India and assessed the suitability of different land covers as reflectance calibration surfaces. Furthermore, we performed LULC classification using different reflectance calibration methods such as Internal Area Relative Reflectance, Flat Field Relative Reflectance, and 6S for comparative analysis. Urban VIS signatures extracted from Hyperion image show distinct spectral curves at broader level. Flat Field Relative Reflectance method provides above 90 percent average overall accuracy. An advanced physics-based method such as 6S does not provide any added advantage over image-based calibration methods. Numéro de notice : A2017-191 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.5.365 En ligne : https://doi.org/10.14358/PERS.83.5.365 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84801
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 5 (May 2017) . - pp 365 - 376[article]Forestry applications of UAVs in Europe: a review / Chiara Torresan in International Journal of Remote Sensing IJRS, vol 38 n° 8-10 (April 2017)
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Titre : Forestry applications of UAVs in Europe: a review Type de document : Article/Communication Auteurs : Chiara Torresan, Auteur ; Andrea Berton, Auteur ; Federico Caretenuto, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 2427 - 2447 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] classification
[Termes IGN] données dendrométriques
[Termes IGN] données lidar
[Termes IGN] drone
[Termes IGN] image hyperspectrale
[Termes IGN] rayonnement lumineux
[Termes IGN] règlement
[Termes IGN] surveillance forestièreRésumé : (auteur) Unmanned aerial vehicles (UAVs) or remotely piloted aircraft systems are new platforms that have been increasingly used over the last decade in Europe to collect data for forest research, thanks to the miniaturization and cost reduction of GPS receivers, inertial navigation system, computers, and, most of all, sensors for remote sensing.
In this review, after describing the regulatory framework for the operation of UAVs in the European Union (EU), an overview of applications in forest research is presented, followed by a discussion of the results obtained from the analysis of different case studies.
Rotary-wing and fixed-wing UAVs are equally distributed among the case studies, while ready-to-fly solutions are preferred over self-designed and developed UAVs. Most adopted technologies are visible-red, green, and blue, multispectral in visible and near-infrared, middle-infrared, thermal infrared imagery, and lidar.
The majority of current UAV-based applications for forest research aim to inventory resources, map diseases, classify species, monitor fire and its effects, quantify spatial gaps, and estimate post-harvest soil displacement.
Successful implementation of UAVs in forestry depends on UAV features, such as flexibility of use in flight planning, low cost, reliability and autonomy, and capability of timely provision of high-resolution data.
Unfortunately, the fragmented regulations among EU countries, a result of the lack of common rules for operating UAVs in Europe, limit the chance to operate within Europe’s boundaries and prevent research mobility and exchange opportunities. Nevertheless, the applications of UAVs are expanding in different domains, and the use of UAVs in forestry will increase, possibly leading to a regular utilization for small-scale monitoring purposes in Europe when recent technologies (i.e. hyperspectral imagery and lidar) and methodological approaches will be consolidated.Numéro de notice : A2017-681 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2016.1252477 En ligne : http://dx.doi.org/10.1080/01431161.2016.1252477 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87243
in International Journal of Remote Sensing IJRS > vol 38 n° 8-10 (April 2017) . - pp 2427 - 2447[article]Hyperspectral band selection from statistical wavelet models / Siwei Feng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 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)PermalinkToward optimum fusion of thermal hyperspectral and visible images in classification of urban area / Farhad Samadzadegan in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 4 (April 2017)PermalinkTransferability of multi- and hyperspectral optical biocrust indices / Emilio Rodríguez-Caballero in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)PermalinkAdaptive linear spectral mixture analysis / Chein-I Chang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkDictionary learning-based feature-level domain adaptation for cross-scene hyperspectral image classification / Minchao Ye in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkDiscriminative low-rank Gabor filtering for spectral–spatial hyperspectral image classification / Lin He in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkExtracting target spectrum for hyperspectral target detection : an adaptive weighted learning method using a self-completed background dictionary / Yubin Niu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkHyperspectral SAR / Matthew Ferrara in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkModified residual method for the estimation of noise in hyperspectral images / Asad Mahmood in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)Permalink