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Increasing robustness of postclassification change detection using time series of land cover maps / Pieter Kempeneers in IEEE Transactions on geoscience and remote sensing, vol 50 n° 9 (October 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 9 (October 2012) . - pp 3327 - 3339 Titre : Increasing robustness of postclassification change detection using time series of land cover maps Type de document : Article/Communication Auteurs : Pieter Kempeneers, Auteur ; F. Sedano, Auteur ; Peter Strobl, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 3327 - 3339 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] détection de changement [Termes IGN] Europe (géographie politique) [Termes IGN] image Terra-MODIS [Termes IGN] incendie de forêt [Termes IGN] méthode robuste [Termes IGN] occupation du sol [Termes IGN] risque naturel [Termes IGN] série temporelle [Termes IGN] surveillance de la végétation Résumé : (Auteur) The monitoring of land cover requires that stable land cover classes be distinguished from changes over time. Within this paper, a postclassification method is presented that provides land cover change information, based on a time series of land cover maps. The method applies a kernel filter to sequential land cover maps. Under some basic assumptions, it shows robustness against classification errors. Despite seasonality, land cover changes often occur at a low temporal frequency (e.g., maximum once every 5-10 years). If land cover maps are available more frequently, some of the information will become redundant (oversampling). The proposed method uses this redundancy for tolerating (nonsystematic) misclassifications. In order to demonstrate the benefits and limitations of the proposed method, analytical expressions have been derived. When compared to a simple postclassification comparison, one of the key strengths of the proposed approach is that it is able to improve both the overall and user's accuracy of change, while also maintaining the same level of producer's accuracy. As a case study, MODerate Resolution Imaging Spectroradiometer remote sensing data from 2006-2010 were classified into forest (F)/nonforest (NF) at pan-European scale. Promising results were obtained for detecting forest loss due to natural disasters. Quality was assessed using burnt area maps in southern Europe and a forest damage report after a windstorm in France. Results indicated a considerable reduction of change detection errors, confirming the theoretical results. Numéro de notice : A2012-448 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2181854 Date de publication en ligne : 21/02/2012 En ligne : https://doi.org/10.1109/TGRS.2011.2181854 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31894 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2012091	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

Multiple endmember unmixing of CHRIS/Proba imagery for mapping impervious surfaces in urban and suburban environments / Luca Demarchi in IEEE Transactions on geoscience and remote sensing, vol 50 n° 9 (October 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 9 (October 2012) . - pp 3409 - 3424 Titre : Multiple endmember unmixing of CHRIS/Proba imagery for mapping impervious surfaces in urban and suburban environments Type de document : Article/Communication Auteurs : Luca Demarchi, Auteur ; Frank Canters, Auteur ; Jonathan Cheung-Wai Chan, Auteur ; T. Van De Voorde, Auteur Année de publication : 2012 Article en page(s) : pp 3409 - 3424 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de mélange spectral d’extrémités multiples [Termes IGN] Flandre (Belgique) [Termes IGN] image PROBA-CHRIS [Termes IGN] occupation du sol [Termes IGN] précision infrapixellaire [Termes IGN] surface imperméable [Termes IGN] zone urbaine Résumé : (Auteur) In this paper, the potential of Compact High-Resolution Imaging Spectrometer (CHRIS)/Project for On-Board Autonomy data for impervious surface mapping is tested in a mixed urban/suburban/rural environment including part of the city of Leuven (Belgium) using multiple endmember unmixing. Various unmixing scenarios are compared, using different threshold values for the RMSE criterion applied to select the proper model for unmixing each pixel. Validation based on 25-cm aerial photography shows that the use of threshold values that favor the application of models with a small number of endmembers performs better compared to scenarios that make use of models with more endmembers. Detailed analysis of model selection for pixels with different land-cover composition indicates that the error in fraction estimation is partly related to spectral confusion between impervious surface types and bare soil, leading to the selection of inappropriate models for the unmixing. In spite of the spectral similarity of soil and impervious surface endmembers, average fractional error for impervious surfaces, vegetation, and bare soil is around 15%, which demonstrates the potential of CHRIS data for mapping the major physical components of the urban/suburban environment at the subpixel scale. Numéro de notice : A2012-449 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2181853 Date de publication en ligne : 15/03/2012 En ligne : https://doi.org/10.1109/TGRS.2011.2181853 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31895 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2012091	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

A complete processing chain for shadow detection and reconstruction in VHR images / L. Lorenzi in IEEE Transactions on geoscience and remote sensing, vol 50 n° 9 (October 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 9 (October 2012) . - pp 3440 - 3452 Titre : A complete processing chain for shadow detection and reconstruction in VHR images Type de document : Article/Communication Auteurs : L. Lorenzi, Auteur ; F. Melgani, Auteur ; Grégoire Mercier, Auteur Année de publication : 2012 Article en page(s) : pp 3440 - 3452 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] détection d'ombre [Termes IGN] image à très haute résolution [Termes IGN] interpolation linéaire [Termes IGN] reconstruction d'image [Termes IGN] régression linéaire Résumé : (Auteur) The presence of shadows in very high resolution (VHR) images can represent a serious obstacle for their full exploitation. This paper proposes to face this problem as a whole through the proposal of a complete processing chain, which relies on various advanced image processing and pattern recognition tools. The first key point of the chain is that shadow areas are not only detected but also classified to allow their customized compensation. The detection and classification tasks are implemented by means of the state-of-the-art support vector machine approach. A quality check mechanism is integrated in order to reduce subsequent misreconstruction problems. The reconstruction is based on a linear regression method to compensate shadow regions by adjusting the intensities of the shaded pixels according to the statistical characteristics of the corresponding nonshadow regions. Moreover, borders are explicitly handled by making use of adaptive morphological filters and linear interpolation for the prevention of possible border artifacts in the reconstructed image. Experimental results obtained on three VHR images representing different shadow conditions are reported, discussed, and compared with two other reconstruction techniques. Numéro de notice : A2012-450 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2183876 Date de publication en ligne : 05/03/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2183876 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31896 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2012091	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

Information fusion in the redundant-wavelet-transform domain for noise-robust hyperspectral classification / S. Prasad in IEEE Transactions on geoscience and remote sensing, vol 50 n° 9 (October 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 9 (October 2012) . - pp 3474 - 3486 Titre : Information fusion in the redundant-wavelet-transform domain for noise-robust hyperspectral classification Type de document : Article/Communication Auteurs : S. Prasad, Auteur ; J. Fowler, Auteur ; L. Bruce, Auteur ; W. Li, Auteur Année de publication : 2012 Article en page(s) : pp 3474 - 3486 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification dirigée [Termes IGN] filtrage du bruit [Termes IGN] fusion de données [Termes IGN] image hyperspectrale [Termes IGN] méthode robuste [Termes IGN] partitionnement [Termes IGN] redondance de données [Termes IGN] transformation en ondelettes Résumé : (Auteur) Hyperspectral imagery comprises high-dimensional reflectance vectors representing the spectral response over a wide range of wavelengths per pixel in the image. The resulting high-dimensional feature spaces often result in statistically ill-conditioned class-conditional distributions. Conventional methods for alleviating this problem typically employ dimensionality reduction such as linear discriminant analysis along with single-classifier systems, yet these methods are suboptimal and lack noise robustness. In contrast, a divide-and-conquer approach is proposed to address the high dimensionality of hyperspectral data for effective and noise-robust classification. Central to the proposed framework is a redundant wavelet transform for representing the data in a feature space amenable to noise-robust multiscale analysis as well as a multiclassifier and decision-fusion system for classification and target recognition in high-dimensional spaces under small-sample-size conditions. The proposed partitioning of this feature space assigns a collection of all coefficients across all scales at a particular spectral wavelength to a dedicated classifier. It is demonstrated that such a partitioning of the feature space for a multiclassifier system yields superior noise performance for classification tasks. Additionally, validation studies with experimental hyperspectral data show that the proposed system significantly outperforms conventional denoising and classification approaches. Numéro de notice : A2012-451 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2185053 Date de publication en ligne : 06/03/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2185053 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31897 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2012091	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt