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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]Deep supervised and contractive neural network for SAR image classification / Jie Geng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
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Titre : Deep supervised and contractive neural network for SAR image classification Type de document : Article/Communication Auteurs : Jie Geng, Auteur ; Hongyu Wang, Auteur ; Jianchao Fan, Auteur ; Xiaorui Ma, Auteur Année de publication : 2017 Article en page(s) : pp 2442 - 2459 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] algorithme Graph-Cut
[Termes IGN] analyse discriminante
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre de déchatoiement
[Termes IGN] filtre de Gabor
[Termes IGN] image radar moirée
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)Résumé : (Auteur) The classification of a synthetic aperture radar (SAR) image is a significant yet challenging task, due to the presence of speckle noises and the absence of effective feature representation. Inspired by deep learning technology, a novel deep supervised and contractive neural network (DSCNN) for SAR image classification is proposed to overcome these problems. In order to extract spatial features, a multiscale patch-based feature extraction model that consists of gray level-gradient co-occurrence matrix, Gabor, and histogram of oriented gradient descriptors is developed to obtain primitive features from the SAR image. Then, to get discriminative representation of initial features, the DSCNN network that comprises four layers of supervised and contractive autoencoders is proposed to optimize features for classification. The supervised penalty of the DSCNN can capture the relevant information between features and labels, and the contractive restriction aims to enhance the locally invariant and robustness of the encoding representation. Consequently, the DSCNN is able to produce effective representation of sample features and provide superb predictions of the class labels. Moreover, to restrain the influence of speckle noises, a graph-cut-based spatial regularization is adopted after classification to suppress misclassified pixels and smooth the results. Experiments on three SAR data sets demonstrate that the proposed method is able to yield superior classification performance compared with some related approaches. Numéro de notice : A2017-176 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2645226 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2645226 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84748
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 2442 - 2459[article]Integrating cellular automata and Markov techniques to generate urban development potential surface : a study on Kolkata agglomeration / Biswajit Mondal in Geocarto international, vol 32 n° 4 (April 2017)
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Titre : Integrating cellular automata and Markov techniques to generate urban development potential surface : a study on Kolkata agglomeration Type de document : Article/Communication Auteurs : Biswajit Mondal, Auteur ; Dipendra Nath Das, Auteur ; Basudeb Bhatta, Auteur Année de publication : 2017 Article en page(s) : pp 401 - 419 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] allocation
[Termes IGN] automate cellulaire
[Termes IGN] Calcutta
[Termes IGN] chaîne de Markov
[Termes IGN] croissance urbaine
[Termes IGN] Inde
[Termes IGN] modèle de simulation
[Termes IGN] prospective
[Termes IGN] répartition géographique
[Termes IGN] surveillance de l'urbanisation
[Termes IGN] urbanisation
[Termes IGN] utilisation du solRésumé : (Résumé) Uncontrolled, yet fragmented peripheral urban expansion has emerged as a menace to urban development. To cope with this rapid urban expansion process, identification of the forces responsible for this rapid urban expansion is a pre-requisite, especially when its threats to habitability are taken into consideration. This study tries to evaluate fragmented uncontrolled urban expansion faced by Kolkata using cellular automata-Markov chain. Urban growth patterns, land use/land cover transformations and spatial allocation correspondence with planning strategy is the main theme of this study. Depending upon the driving forces, the study result indicates a bi-directional urban development potential surface, which might be a result of the biased planning initiative along with middle-class residential demand. This simulation result provides evidence for the planning authority to evaluate the effectiveness of spatial allocation and urban expansion trends and provide flexibility to modify the current allocation scenario. Numéro de notice : A2017-272 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1155656 Date de publication en ligne : 23/03/2016 En ligne : http://doi.org/10.1080/10106049.2016.1155656 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85299
in Geocarto international > vol 32 n° 4 (April 2017) . - pp 401 - 419[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2017041 RAB Revue Centre de documentation En réserve L003 Disponible Spatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration / Yinghai Ke in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)
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Titre : Spatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration Type de document : Article/Communication Auteurs : Yinghai Ke, Auteur ; Jungho Im, Auteur ; Seonyoung Park, Auteur ; Huili Gong, Auteur Année de publication : 2017 Article en page(s) : pp 79 – 93 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] évapotranspiration
[Termes IGN] image à haute résolution
[Termes IGN] image Terra-MODIS
[Termes IGN] indice de végétation
[Termes IGN] réflectance de surface
[Termes IGN] ressources en eau
[Termes IGN] température au solRésumé : (auteur) Continuous monitoring of actual evapotranspiration (ET) is critical for water resources management at both regional and local scales. Although the MODIS ET product (MOD16A2) provides viable sources for ET monitoring at 8-day intervals, the spatial resolution (1 km) is too coarse for local scale applications. In this study, we propose a machine learning and spatial temporal fusion (STF)-integrated approach in order to generate 8-day 30 m ET based on both MOD16A2 and Landsat 8 data with three schemes. Random forest machine learning was used to downscale MODIS 1 km ET to 30 m resolution based on nine Landsat-derived indicators including vegetation indices (VIs) and land surface temperature (LST). STF-based models including Spatial and Temporal Adaptive Reflectance Fusion Model and Spatio-Temporal Image Fusion Model were used to derive synthetic Landsat surface reflectance (scheme 1)/VIs (scheme 2)/ET (scheme 3) on Landsat-unavailable dates. The approach was tested over two study sites in the United States. The results showed that fusion of Landsat VIs produced the best accuracy of predicted ET (R2 = 0.52–0.97, RMSE = 0.47–3.0 mm/8 days and rRMSE = 6.4–37%). High density of cloud-clear Landsat image acquisitions and low spatial heterogeneity of Landsat VIs benefit the ET prediction. The downscaled 30 m ET had good agreement with MODIS ET (RMSE = 0.42–3.4 mm/8 days, rRMSE = 3.2–26%). Comparison with the in situ ET measurements showed that the downscaled ET had higher accuracy than MODIS ET. Numéro de notice : A2017-114 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.02.006 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2017.02.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84509
in ISPRS Journal of photogrammetry and remote sensing > vol 126 (April 2017) . - pp 79 – 93[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017041 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017043 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017042 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Unsupervised feature learning for land-use scene recognition / Jiayuan Fan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)PermalinkClassifying natural-language spatial relation terms with random forest algorithm / Shihong Du in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)PermalinkCognitively plausible representations for the alignment of sketch and geo-referenced maps / Sahib Jan in Journal of Spatial Information Science, JoSIS, n° 14 (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)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)PermalinkA hybrid genetic algorithm with local optimiser improves calibration of a vegetation change cellular automata model / Rachel Whitsed in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)PermalinkPredicting the encoding of secondary diagnoses. An experience based on decision trees / Ghazar Chahbandarian in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 22 n° 2 (mars - avril 2017)PermalinkTowards a knowledge base to support geoprocessing workflow development / Barbara Hofer in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)PermalinkLe travail de la science et le numérique : Données, publications, plateformes / Direction de l'information scientifique et technique - CNRS (20/02/2017)PermalinkJoint sparse representation and multitask learning for hyperspectral target detection / Yuxiang Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)Permalink