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RPC-based coregistration of VHR imagery for urban change detection / Shabnam Jabari in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)
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Titre : RPC-based coregistration of VHR imagery for urban change detection Type de document : Article/Communication Auteurs : Shabnam Jabari, Auteur ; Yun Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 521 - 534 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] angle de visée
[Termes IGN] coefficient de corrélation
[Termes IGN] détection de changement
[Termes IGN] image à très haute résolution
[Termes IGN] image Geoeye
[Termes IGN] image Ikonos
[Termes IGN] image multitemporelle
[Termes IGN] image Worldview
[Termes IGN] milieu urbain
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle par fonctions rationnelles
[Termes IGN] points homologuesRésumé : (Auteur) In urban change detection, coregistration between bi-temporal Very High Resolution (VHR) images taken from different viewing angles, especially from high off-nadir angles, is very challenging. The relief displacements of elevated objects in such images usually lead to significant misregistration that negatively affects the accuracy of change detection. This paper presents a novel solution, called Patch-Wise CoRegistration (PWCR), that can overcome the misregistration problem caused by viewing angle difference and accordingly improve the accuracy of urban change detection. The PWCR method utilizes a Digital Surface Model (DSM) and the Rational Polynomial Coefficients (RPCs) of the images to find corresponding points in a bi-temporal image set. The corresponding points are then used to generate corresponding patches in the image set. To prove that the PWCR method can overcome the misregistration problem and help achieving accurate change detection, two change detection criteria are tested and incorporated into a change detection framework. Experiments on four bi-temporal image sets acquired by Ikonos, GeoEye-1, and Worldview-2 satellites from different viewing angles show that the PWCR method can achieve highly accurate image patch coregistration (up to 80 percent higher than traditional coregistration for elevated objects), so that the change detection framework can produce accurate urban change detection results (over 90 percent). Numéro de notice : A2016-514 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 0.14358/PERS.82.7.521 En ligne : http://dx.doi.org/10.14358/PERS.82.7.521 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81585
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 7 (juillet 2016) . - pp 521 - 534[article]Sparse and low-rank graph for discriminant analysis of hyperspectral imagery / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
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Titre : Sparse and low-rank graph for discriminant analysis of hyperspectral imagery Type de document : Article/Communication Auteurs : Wei Li, Auteur ; Jiabin Liu, Auteur ; Qian Du, Auteur Année de publication : 2016 Article en page(s) : pp 4094 - 4105 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] analyse en composantes principales
[Termes IGN] graphe
[Termes IGN] image hyperspectrale
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] valeur propreRésumé : (Auteur) Recently, sparse graph-based discriminant analysis (SGDA) has been developed for the dimensionality reduction and classification of hyperspectral imagery. In SGDA, a graph is constructed by ℓ1-norm optimization based on available labeled samples. Different from traditional methods (e.g., k-nearest neighbor with Euclidean distance), weights in an ℓ1-graph derived via a sparse representation can automatically select more discriminative neighbors in the feature space. However, the sparsity-based graph represents each sample individually, lacking a global constraint on each specific solution. As a consequence, SGDA may be ineffective in capturing the global structures of data. To overcome this drawback, a sparse and low-rank graph-based discriminant analysis (SLGDA) is proposed. Low-rank representation has been proved to be capable of preserving global data structures, although it may result in a dense graph. In SLGDA, a more informative graph is constructed by combining both sparsity and low rankness to maintain global and local structures simultaneously. Experimental results on several different multiple-class hyperspectral-classification tasks demonstrate that the proposed SLGDA significantly outperforms the state-of-the-art SGDA. Numéro de notice : A2016-879 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2536685 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2536685 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83042
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 7 (July 2016) . - pp 4094 - 4105[article]Fusion of hyperspectral and VHR multispectral image classifications in urban α–areas / Alexandre Hervieu in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-3 (July 2016)
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Titre : Fusion of hyperspectral and VHR multispectral image classifications in urban α–areas Type de document : Article/Communication Auteurs : Alexandre Hervieu , Auteur ; Arnaud Le Bris
, Auteur ; Clément Mallet
, Auteur
Année de publication : 2016 Projets : HYEP / Weber, Christiane Article en page(s) : pp 457 - 464 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] méthode de réduction d'énergie
[Termes IGN] occupation du sol
[Termes IGN] optimisation (mathématiques)
[Termes IGN] zone urbaineRésumé : (auteur) An energetical approach is proposed for classification decision fusion in urban areas using multispectral and hyperspectral imagery at distinct spatial resolutions. Hyperspectral data provides a great ability to discriminate land-cover classes while multispectral data,usually at higher spatial resolution, makes possible a more accurate spatial delineation of the classes. Hence, the aim here is to achieve the most accurate classification maps by taking advantage of both data sources at the decision level: spectral properties of the hyperspectral data and the geometrical resolution of multispectral images. More specifically, the proposed method takes into account probability class membership maps in order to improve the classification fusion process. Such probability maps are available using standard classification techniques such as Random Forests or Support Vector Machines. Classification probability maps are integrated into an energy framework where minimization of a given energy leads to better classification maps. The energy is minimized using a graph-cut method called quadratic pseudo-boolean optimization (QPBO) with α-expansion. A first model is proposed that gives satisfactory results in terms of classification results and visual interpretation. This model is compared to a standard Potts models adapted to the considered problem. Finally, the model is enhanced by integrating the spatial contrast observed in the data source of higher spatial resolution (i.e., the multispectral image). Obtained results using the proposed energetical decision fusion process are shown on two urban multispectral/hyperspectral datasets. 2-3% improvement is noticed with respect to a Potts formulation and 3-8% compared to a single hyperspectral-based classification. Numéro de notice : A2016-826 Affiliation des auteurs : LASTIG MATIS (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-III-3-457-2016 Date de publication en ligne : 06/06/2016 En ligne : http://dx.doi.org/10.5194/isprs-annals-III-3-457-2016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82697
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol III-3 (July 2016) . - pp 457 - 464[article]Documents numériques
en open access
Fusion of hyperspectral and VHR ... - pdf éditeurAdobe Acrobat PDFAn assessment of algorithmic parameters affecting image classification accuracy by random forests / Dee Shi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)
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Titre : An assessment of algorithmic parameters affecting image classification accuracy by random forests Type de document : Article/Communication Auteurs : Dee Shi, Auteur ; Xiaojun Yang, Auteur Année de publication : 2016 Article en page(s) : pp 407 - 417 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] classification
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] impact sur les données
[Termes IGN] occupation du sol
[Termes IGN] précision de la classificationRésumé : (Auteur) Random forests as a promising ensemble learning algorithm have been increasingly used for remote sensor image classification, and are found to perform identical or better than some popular classifiers. With only two algorithmic parameters, they are relatively easier to implement. Existing literature suggests that the performance of random forests is insensitive to changing algorithmic parameters. However, this was largely based on the classifier's accuracy that does not necessarily represent the resulting thematic map accuracy. The current study extends beyond the classifier's accuracy assessment and investigate how the algorithmic parameters could affect the resulting thematic map accuracy by random forests. A set of random forest models with different parameter settings was carefully constructed and then used to classify a satellite image into multiple land cover categories. Both the classifier's accuracy and the map accuracy were assessed. The results reveal that these parameters can affect the map accuracy up to 9 ∼16 percent for some classes, although their impact on the classifier's accuracy was quite limited. A careful parameterization prioritizing thematic map accuracy can help improve the performance of random forests in image classification, especially for spectrally complex land cover classes. These findings can help establish practical guidance on the use of random forests in the remote sensing community. Numéro de notice : A2016-440 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.6.407 En ligne : http://dx.doi.org/10.14358/PERS.82.6.407 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81345
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 6 (June 2016) . - pp 407 - 417[article]An intelligent geospatial processing unit for image classification based on geographic vector agents (GVAs) / Kambiz Borna in Transactions in GIS, vol 20 n° 3 (June 2016)
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Titre : An intelligent geospatial processing unit for image classification based on geographic vector agents (GVAs) Type de document : Article/Communication Auteurs : Kambiz Borna, Auteur ; Antoni B. Moore, Auteur ; Pascal Sirguey, Auteur Année de publication : 2016 Article en page(s) : pp 368–381 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] agent (intelligence artificielle)
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification automatique
[Termes IGN] données lidar
[Termes IGN] données vectorielles
[Termes IGN] image Ikonos
[Termes IGN] modèle orienté agentRésumé : (auteur) Spatial modeling methods usually use pixels and image objects as fundamental processing units to address real-world objects, geo-objects, in image space. To do this, both pixel-based and object-based approaches typically employ a linear two-staged workflow of segmentation and classification. Pixel-based methods segment a classified image to address geo-objects in image space. In contrast, object-based approaches classify a segmented image to identify geo-objects from raster datasets. These methods lack the ability to simultaneously integrate the geometry and theme of geo-objects in image space. This article explores Geographical Vector Agents (GVAs) as an automated and intelligent processing unit to directly address real-world objects in the process of remote sensing image classification. The GVA is a distinct type of geographic automata characterized by elastic geometry, dynamic internal structure, neighborhoods and their respective rules. We test this concept by modeling a set of objects on a subset IKONOS image and LiDAR DSM datasets without the setting parameters (e.g. scale, shape information), usually applied in conventional Geographic Object-Based Image Analysis (GEOBIA) approaches. The results show that the GVA approach achieves more than 3.5% improvement for correctness, 2% improvement for quality, although no significant improvement for completeness to GEOBIA, thus demonstrating the competitive performance of GVAs classification. Numéro de notice : A2016-460 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12226 En ligne : http://dx.doi.org/10.1111/tgis.12226 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81390
in Transactions in GIS > vol 20 n° 3 (June 2016) . - pp 368–381[article]An interactive tool for semi-automatic feature extraction of hyperspectral data / Zoltan Kovacs in Open geosciences, vol 8 n° 1 (January - July 2016)
PermalinkAutomated bias-compensation approach for pushbroom sensor modeling using digital elevation model / Kwan-Young Oh in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)
PermalinkCorrection of atmospheric refraction geolocation error for high resolution optical satellite pushbroom images / Ming Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)
PermalinkA spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery / Bei Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)
PermalinkSupervised classification of very high resolution optical images using wavelet-based textural features / Olivier Regniers in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)
PermalinkVector attribute profiles for hyperspectral image classification / Erchan Aptoula in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)
PermalinkAn iterative haze optimized transformation for automatic cloud/haze detection of landsat imagery / Shuli Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)
PermalinkDeep filter banks for texture recognition, description, and segmentation / Mircea Cimpoi in International journal of computer vision, vol 118 n° 1 (May 2016)
PermalinkExploiting joint sparsity for pansharpening : the J-SparseFI algorithm / Xiao Xiang Zhu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)
PermalinkICESat/GLAS canopy height sensitivity inferred from Airborne Lidar / Craig Mahoney in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 5 (May 2016)
PermalinkKernel-based domain-invariant feature selection in hyperspectral images for transfer learning / Claudio Persello in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)
PermalinkMidrange geometric interactions for semantic segmentation / Julia Diebold in International journal of computer vision, vol 117 n° 3 (May 2016)
PermalinkMultiple morphological component analysis based decomposition for remote sensing image classification / Xiang Xu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)
PermalinkA novel automatic structural linear feature-based matching method based on new concepts of mathematically-generated-points and lines / Somayeh Yavari in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 5 (May 2016)
PermalinkRecent developments in large-scale tie-point matching / Wilfried Hartmann in ISPRS Journal of photogrammetry and remote sensing, vol 115 (May 2016)
PermalinkThe georeferencing of RASAT satellite imagery and some practical approaches to increase the georeferencing accuracy / Mustafa Erdogan in Geocarto international, vol 31 n° 5 - 6 (May - June 2016)
PermalinkActive-metric learning for classification of remotely sensed hyperspectral images / Edoardo Pasolli in IEEE Transactions on geoscience and remote sensing, vol 54 n° 4 (April 2016)
PermalinkComparative study on projected clustering methods for hyperspectral imagery classification / Anand Mehta in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)
PermalinkComparison of three Landsat TM compositing methods: A case study using modeled tree canopy cover / Bonnie Ruefenacht in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)
PermalinkA feature selection approach for segmentation of very high-resolution satellite images / Ahmad Izadipour in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)
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