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Learning aggregated features and optimizing model for semantic labeling / Jianhua Wang in The Visual Computer, vol 33 n° 12 (December 2017)
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Titre : Learning aggregated features and optimizing model for semantic labeling Type de document : Article/Communication Auteurs : Jianhua Wang, Auteur ; Chuanxia Zheng, Auteur ; Weihai Chen, Auteur ; Xingming Wu, Auteur Année de publication : 2017 Article en page(s) : pp 1587 - 1600 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] attribut
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image RVB
[Termes IGN] modèle statistique
[Termes IGN] scène intérieure
[Termes IGN] segmentation d'imageRésumé : (Auteur) Semantic labeling for indoor scenes has been extensively developed with the wide availability of affordable RGB-D sensors. However, it is still a challenging task for multi-class recognition, especially for “small” objects. In this paper, a novel semantic labeling model based on aggregated features and contextual information is proposed. Given an RGB-D image, the proposed model first creates a hierarchical segmentation using an adapted gPb/UCM algorithm. Then, a support vector machine is trained to predict initial labels using aggregated features, which fuse small-scale appearance features, mid-scale geometric features, and large-scale scene features. Finally, a joint multi-label Conditional random field model that exploits both spatial and attributive contextual relations is constructed to optimize the initial semantic and attributive predicted results. The experimental results on the public NYU v2 dataset demonstrate the proposed model outperforms the existing state-of-the-art methods on the challenging 40 dominant classes task, and the model also achieves a good performance on a recent SUN RGB-D dataset. Especially, the prediction accuracy of “small” classes has been improved significantly. Numéro de notice : A2017-714 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-016-1302-4 En ligne : https://doi.org/10.1007/s00371-016-1302-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88098
in The Visual Computer > vol 33 n° 12 (December 2017) . - pp 1587 - 1600[article]Multimorphological superpixel model for hyperspectral image classification / Tianzhu Liu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)
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Titre : Multimorphological superpixel model for hyperspectral image classification Type de document : Article/Communication Auteurs : Tianzhu Liu, Auteur ; Yanfeng Gu, Auteur ; Jocelyn Chanussot, Auteur ; Mauro Dalla Mura, Auteur Année de publication : 2017 Article en page(s) : pp 6950 - 6963 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] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] méthode fondée sur le noyauRésumé : (Auteur) With the development of hyperspectral sensors, nowadays, we can easily acquire large amount of hyperspectral images (HSIs) with very high spatial resolution, which has led to a better identification of relatively small structures. Owing to the high spatial resolution, there are much less mixed pixels in the HSIs, and the boundaries between these categories are much clearer. However, the high spatial resolution also leads to complex and fine geometrical structures and high inner-class variability, which make the classification results very “noisy.” In this paper, we propose a multimorphological superpixel (MMSP) method to extract the spectral and spatial features and address the aforementioned problems. To reduce the difference within the same class and obtain multilevel spatial information, morphological features (multistructuring element extended morphological profile or multiattribute filter extended multi-attribute profiles) are first obtained from the original HSI. After that, simple linear iterative clustering segmentation method is performed on each morphological feature to acquire the MMSPs. Then, uniformity constraint is used to merge the MMSPs belonging to the same class which can avoid introducing the information from different classes and acquire spatial structures at object level. Subsequently, mean filtering is utilized to extract the spatial features within and among MMSPs. At last, base kernels are obtained from the spatial features and original HSI, and several multiple kernel learning methods are used to obtain the optimal kernel to incorporate into the support vector machine. Experiments conducted on three widely used real HSIs and compared with several well-known methods demonstrate the effectiveness of the proposed model. Numéro de notice : A2017-767 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2737037 En ligne : https://doi.org/10.1109/TGRS.2017.2737037 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88806
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 12 (December 2017) . - pp 6950 - 6963[article]Single image dehazing via an improved atmospheric scattering model / Mingye Ju in The Visual Computer, vol 33 n° 12 (December 2017)
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Titre : Single image dehazing via an improved atmospheric scattering model Type de document : Article/Communication Auteurs : Mingye Ju, Auteur ; Dengyin Zhang, Auteur ; Xuemei Wang, Auteur Année de publication : 2017 Article en page(s) : pp 1613 - 1625 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] brouillard
[Termes IGN] diffusion du rayonnement
[Termes IGN] effet atmosphérique
[Termes IGN] image isolée
[Termes IGN] scène urbaine
[Termes IGN] segmentation d'imageRésumé : (Auteur) Under foggy or hazy weather conditions, the visibility and color fidelity of outdoor images are prone to degradation. Hazy images can be the cause of serious errors in many computer vision systems. Consequently, image haze removal has practical significance for real-world applications. In this study, we first analyze the inherent weaknesses of the atmospheric scattering model and propose an improvement to address those weaknesses. Then, we present a fast image haze removal algorithm based on the improved model. In our proposed method, the input image is partitioned into several scenes based on the haze thickness. Next, averaging and erosion operations calculate the rough scene luminance map in a scene-wise manner. We obtain the rough scene transmission map by maximizing the contrast in each scene and then develop a way to gently remove the haze using an adaptive method for adjusting scene transmission based on scene features. In addition, we propose a guided total variation model for edge optimization, so as to prevent from the block effect as well as to eliminate the negative effect from the wrong scene segmentation results. The experimental results demonstrate that our method is effective in solving a series of common problems, including uneven illuminance, overenhanced and oversaturated images, and so forth. Moreover, our method outperforms most current dehazing algorithms in terms of visual effects, universality, and processing speed. Numéro de notice : A2017-715 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-016-1305-1 En ligne : https://doi.org/10.1007/s00371-016-1305-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88099
in The Visual Computer > vol 33 n° 12 (December 2017) . - pp 1613 - 1625[article]Use of unsupervised classification for the determination of prevailing land use typology / Miha Konjar in Geodetski vestnik, vol 61 n° 4 (December 2017 - February 2018)
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Titre : Use of unsupervised classification for the determination of prevailing land use typology Type de document : Article/Communication Auteurs : Miha Konjar, Auteur ; Alma Zavodnik Lamovsek, Auteur ; Dejan Grigillo, Auteur Année de publication : 2017 Article en page(s) : pp 541 - 581 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] agrégation spatiale
[Termes IGN] classification non dirigée
[Termes IGN] complexité
[Termes IGN] densité de population
[Termes IGN] données socio-économiques
[Termes IGN] image numérique
[Termes IGN] indicateur spatial
[Termes IGN] occupation du sol
[Termes IGN] Slovénie
[Termes IGN] utilisation du sol
[Termes IGN] zone homogèneRésumé : (Auteur) This paper presents classification methods that enable the division of space into homogeneous areas that combine the spatial characteristics with influence on land use and changes thereof. It was determined that the existing methods do not always include the criteria needed for the aggregation of spatial units into homogeneous groups. The results of the analysis showed that the identified homogenous groups do not fully capture the spatial complexity and diversity important for land use change analyses. For this reason, a new approach to the classification of spatial units based on the unsupervised classification of digital images was proposed. The methodology includes the selection of appropriate indicators, that consider land use more comprehensively and thus enable better classification results. The use of the unsupervised classification method for prevailing land use typology has been tested in Slovenia. At the municipal level, seven types of prevailing land use were identified. Numéro de notice : A2017-777 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.15292//geodetski-vestnik.2017.04.541-581 En ligne : http://www.geodetski-vestnik.com/61/4/gv61-4_konjar.pdf Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88981
in Geodetski vestnik > vol 61 n° 4 (December 2017 - February 2018) . - pp 541 - 581[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 139-2017041 RAB Revue Centre de documentation En réserve L003 Disponible A batch-mode regularized multimetric active learning framework for classification of hyperspectral images / Zhou Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)
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Titre : A batch-mode regularized multimetric active learning framework for classification of hyperspectral images Type de document : Article/Communication Auteurs : Zhou Zhang, Auteur ; Melba M. Crawford, Auteur Année de publication : 2017 Article en page(s) : pp 6594 - 6609 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification barycentrique
[Termes IGN] image hyperspectrale
[Termes IGN] précision de la classificationRésumé : (Auteur) Techniques that combine multiple types of features, such as spectral and spatial features, for hyperspectral image classification can often significantly improve the classification accuracy and produce a more reliable thematic map. However, the high dimensionality of the input data and the typically limited quantity of labeled samples are two key challenges that affect classification performance of supervised methods. In order to simultaneously deal with these issues, a regularized multimetric active learning (AL) framework is proposed which consists of three main parts. First, a regularized multimetric learning approach is proposed to jointly learn distinct metrics for different types of features. The regularizer incorporates the unlabeled data based on the neighborhood relationship, which helps avoid overfitting at early stages of AL, when the quantity of training data is particularly small. Then, as AL proceeds, the regularizer is also updated through similarity propagation, thus taking advantage of informative labeled samples. Finally, multiple features are projected into a common feature space, in which a new batch-mode AL strategy combining uncertainty and diversity is utilized in conjunction with k-nearest neighbor classification to enrich the set of labeled samples. In order to evaluate the effectiveness of the proposed framework, the experiments were conducted on two benchmark hyperspectral data sets, and the results were compared to those achieved by several other state-of-the-art AL methods. Numéro de notice : A2017-760 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2730583 En ligne : https://doi.org/10.1109/TGRS.2017.2730583 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88788
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6594 - 6609[article]A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data / Qunying Huang in Computers, Environment and Urban Systems, vol 66 (November 2017)
PermalinkFusing microwave and optical satellite observations to simultaneously retrieve surface soil moisture, vegetation water content, and surface soil roughness / Yohei Sawada in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)
PermalinkFusion of hyperspectral and LiDAR data using sparse and low-rank component analysis / Behnood Rasti in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)
PermalinkRobust minimum volume simplex analysis for hyperspectral unmixing / Shaoquan Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)
PermalinkSparse distributed multitemporal hyperspectral unmixing / Jakob Sigurdsson in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)
PermalinkSpatial group sparsity regularized nonnegative matrix factorization for hyperspectral unmixing / Xinyu Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)
PermalinkThe Naïve Overfitting Index Selection (NOIS): A new method to optimize model complexity for hyperspectral data / Alby D. Rocha in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)
PermalinkAn effective spherical panoramic LoD model for a mobile street view service / Xianxiong Liu in Transactions in GIS, vol 21 n° 5 (October 2017)
PermalinkHyperspectral dimensionality reduction for biophysical variable statistical retrieval / Juan Pablo Rivera-Caicedo in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)
PermalinkHyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables / Sakari Tuominen in Silva fennica, vol 51 n° 5 (2017)
PermalinkKinetic depth images: flexible generation of depth perception / Sujal Bista in The Visual Computer, vol 33 n° 10 (October 2017)
PermalinkA structured regularization framework for spatially smoothing semantic labelings of 3D point clouds / Loïc Landrieu in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)
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PermalinkUnderstanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications / Amanda Veloso in Remote sensing of environment, vol 199 (15 September 2017)
PermalinkBand subset selection for anomaly detection in hyperspectral imagery / Lin Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
PermalinkRemote sensing scene classification by unsupervised representation learning / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
PermalinkSelf-calibration of omnidirectional multi-cameras including synchronization and rolling shutter / Thanh-Tin Nguyen in Computer Vision and image understanding, vol 162 (September 2017)
PermalinkUnsupervised domain adaptation for early detection of drought stress in hyperspectral images / P. Schmitter in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)
PermalinkUsing landsat surface reflectance data as a reference target for multiswath hyperspectral data collected over mixed agricultural rangeland areas / Cooper McCann in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
PermalinkColour Helmholtz stereopsis for reconstruction of dynamic scenes with arbitrary unknown reflectance / Nadejda Roubtsova in International journal of computer vision, vol 124 n° 1 (August 2017)
PermalinkFrom subpixel to superpixel : a novel fusion framework for hyperspectral image classification / Ting Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkIntersensor statistical matching for pansharpening : theoretical issues and practical solutions / Luciano Alparone in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkLearning and transferring deep joint spectral–spatial features for hyperspectral classification / Jingxiang Yang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkLearning a discriminative distance metric with label consistency for scene classification / Yuebin Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkLearning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks / Shaohui Mei in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkMorphologically decoupled structured sparsity for rotation-invariant hyperspectral image analysis / Saurabh Prasad in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkMulti-view performance capture of surface details / Nadia Robertini in International journal of computer vision, vol 124 n° 1 (August 2017)
PermalinkA novel preunmixing framework for efficient detection of linear mixtures in hyperspectral images / Andrea Marinoni in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkPotential application of remote sensing in monitoring ecosystem services of forests, mangroves and urban areas / Ram Avtar in Geocarto international, vol 32 n° 8 (August 2017)
PermalinkSimultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks / Rasha Alshehhi in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
PermalinkSuperpixel-based intrinsic image decomposition of hyperspectral images / Xudong Jin in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkDeveloping detailed age-specific thematic maps for coffee (Coffea arabica L.) in heterogeneous agricultural landscapes using random forests applied on Landsat 8 multispectral sensor / Abel Chemura in Geocarto international, vol 32 n° 7 (July 2017)
PermalinkFusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area / Mohamed Barakat A. Gibril in Geocarto international, vol 32 n° 7 (July 2017)
PermalinkJoint hyperspectral superresolution and unmixing with interactive feedback / Chen Yi in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)
PermalinkNorthern conifer forest species classification using multispectral data acquired from an unmanned aerial vehicle / Steven E. Franklin in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 7 (July 2017)
PermalinkPolarGlobe : A web-wide virtual globe system for visualizing multidimensional, time-varying, big climate data / Wenwen Li in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)
PermalinkTotal variation regularized reweighted sparse nonnegative matrix factorization for hyperspectral unmixing / Wei He in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)
PermalinkAutomatic illumination-invariant image-to-geometry registration in outdoor environments / Christian Kehl in Photogrammetric record, vol 32 n° 158 (June - july 2017)
PermalinkDescribing contrast across scales / Sohaib Ali Syed in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)
PermalinkIntegration of SSC TerraSAR-X images into multisource rapid mapping / D. Vassilaki in Photogrammetric record, vol 32 n° 158 (June - july 2017)
PermalinkLearning to diversify deep belief networks for hyperspectral image classification / Ping Zhong in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)
PermalinkA novel semisupervised active-learning algorithm for hyperspectral image classification / Zengmao Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)
PermalinkObject-based analysis of multispectral airborne laser scanner data for land cover classification and map updating / Leena Matikainen in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)
PermalinkPan-sharpening of Landsat-8 images and its application in calculating vegetation greenness and canopy water contents / Khan Rubayet Rahaman in ISPRS International journal of geo-information, vol 6 n° 6 (June 2017)
PermalinkDisocclusion of 3D LiDAR point clouds using range images / Pierre Biasutti in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-1/W1 (May 2017)
PermalinkSemiautomatic detection and classification of materials in historic buildings with low-cost photogrammetric equipment / Javier Sanchez in Journal of Cultural Heritage, vol 25 (May - June 2017)
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