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An examination of diameter density prediction with k-NN and airborne lidar / Jacob L. Strunk in Forests, vol 8 n° 11 (November 2017)  

Public [article]  inForests > vol 8 n° 11 (November 2017) Titre : An examination of diameter density prediction with k-NN and airborne lidar Type de document : Article/Communication Auteurs : Jacob L. Strunk, Auteur ; Peter J. Gould, Auteur ; Petteri Packalen, Auteur ; Krishna P. Poudel, Auteur ; Hans-Erik Andersen, Auteur ; Hailemariam Temesgen, Auteur Année de publication : 2017 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Caroline du Sud (Etats-Unis) [Termes IGN] classification barycentrique [Termes IGN] classification par la distance de Mahalanobis [Termes IGN] diamètre des arbres [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] lasergrammétrie [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) While lidar-based forest inventory methods have been widely demonstrated, performances of methods to predict tree diameters with airborne lidar (lidar) are not well understood. One cause for this is that the performance metrics typically used in studies for prediction of diameters can be difficult to interpret, and may not support comparative inferences between sampling designs and study areas. To help with this problem we propose two indices and use them to evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions. The indices are based on the coefficient of determination (R2), and root mean square deviation (RMSD). Both of the indices are highly interpretable, and the RMSD-based index facilitates comparisons with alternative (non-lidar) inventory strategies, and with projects in other regions. K-NN diameter distribution prediction strategies were examined using auxiliary lidar for 190 training plots distribute across the 800 km2 Savannah River Site in South Carolina, USA. We evaluate the performance of k-NN with respect to distance metrics, number of neighbors, predictor sets, and response sets. K-NN and lidar explained 80% of variability in diameters, and Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria Numéro de notice : A2017-877 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f8110444 Date de publication en ligne : 16/11/2017 En ligne : https://doi.org/10.3390/f8110444 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91213 [article]

Change detection in forests and savannas using statistical analysis based on geographical objects / Lucilia Rezende Leite in Boletim de Ciências Geodésicas, vol 23 n° 2 (abr - jun 2017)  

Public [article]  inBoletim de Ciências Geodésicas > vol 23 n° 2 (abr - jun 2017) . - pp 284 - 295 Titre : Change detection in forests and savannas using statistical analysis based on geographical objects Type de document : Article/Communication Auteurs : Lucilia Rezende Leite, Auteur ; Luis Marcelo Tavares de Carvalho, Auteur ; Fortunato Menezes da Silva, Auteur Année de publication : 2017 Article en page(s) : pp 284 - 295 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] analyse diachronique [Termes IGN] Brésil [Termes IGN] classification par la distance de Mahalanobis [Termes IGN] détection de changement [Termes IGN] forêt équatoriale [Termes IGN] image Landsat-TM [Termes IGN] khi carré [Termes IGN] réflectance végétale [Termes IGN] savane [Termes IGN] segmentation d'image Résumé : (auteur) The aim of this work was to assess techniques of land cover change detection in areas of Brazilian Forest and Savanna, using Landsat 5/TM images, and two iterative statistical methodologies based on geographical objects. The sensitivity of the methodologies was assessed in relation to the heterogeneity of the input data, the use of reflectance data and vegetation indices, and the use of different levels of confidence. The periods analyzed were from 2000 to 2006, and from 2006 to 2010. After the segmentation of images, the descriptive statistics average and standard deviation of each object were extracted. The determination of change objects was realized in an iterative way based on the Mahalanobis Distance and the chi-square distribution. The results were validated with an early visual detection and analyzed according to Receiver Operating Characteristic (ROC) Curve. Significant gains were obtained by using vegetation masks and bands 3 and 4 for both areas tested with 94,67% and 95,02% of the objects correctly detected as changes, respectively for the areas of Forest and Savanna. The use of the NDVI and different images were not satisfactory in this study. Numéro de notice : A2017-394 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1590/S1982-21702017000200018 En ligne : http://dx.doi.org/10.1590/S1982-21702017000200018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85910 [article]

Applying six classifiers to airborne hyperspectral imagery for detecting giant reed / C. Yang in Geocarto international, vol 27 n° 5 (August 2012)  

Public [article]  inGeocarto international > vol 27 n° 5 (August 2012) . - pp 413 - 424 Titre : Applying six classifiers to airborne hyperspectral imagery for detecting giant reed Type de document : Article/Communication Auteurs : C. Yang, Auteur ; J. Goolsby, Auteur ; James H. Everitt, Auteur ; Q. Du, Auteur Année de publication : 2012 Article en page(s) : pp 413 - 424 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] classificateur [Termes IGN] classification barycentrique [Termes IGN] classification par la distance de Mahalanobis [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification Spectral angle mapper [Termes IGN] espèce exotique envahissante [Termes IGN] Etats-Unis [Termes IGN] image aérienne [Termes IGN] image hyperspectrale [Termes IGN] macrophyte [Termes IGN] Mexique [Termes IGN] Rio Grande (fleuve) Résumé : (Auteur) This study evaluated and compared six image classifiers, including minimum distance (MD), Mahalanobis distance (MAHD), maximum likelihood (ML), spectral angle mapper (SAM), mixture tuned matched filtering (MTMF) and support vector machine (SVM), for detecting and mapping giant reed (Arundo donax L.), an invasive weed that presents a severe threat to agroecosystems throughout the southern US and northern Mexico. Airborne hyperspectral imagery was collected from a giant reed-infested site along the US-Mexican portion of the Rio Grande in 2009 and 2010. The imagery was transformed with minimum noise fraction (MFN) and the six classifiers were applied to the 30-band MNF imagery for each year. Accuracy assessment showed that SVM and ML generally performed better than the other four classifiers for overall classification and for distinguishing giant reed in both years. These results indicate that airborne hyperspectral imagery in conjunction with SVM and ML classification techniques is effective for detecting giant reed. Numéro de notice : A2012-371 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2011.643321 Date de publication en ligne : 04/01/2012 En ligne : https://doi.org/10.1080/10106049.2011.643321 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31817 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2012051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Evaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas gulf coast / C. Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 4 (April 2009)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 75 n° 4 (April 2009) . - pp 425 - 435 Titre : Evaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas gulf coast Type de document : Article/Communication Auteurs : C. Yang, Auteur ; James H. Everitt, Auteur ; R.S. Fletcher, Auteur Année de publication : 2009 Article en page(s) : pp 425 - 435 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte de la végétation [Termes IGN] classification barycentrique [Termes IGN] classification par la distance de Mahalanobis [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification spectrale [Termes IGN] image aérienne [Termes IGN] image AISA+ [Termes IGN] image hyperspectrale [Termes IGN] Kappa de Cohen [Termes IGN] littoral [Termes IGN] mangrove [Termes IGN] Mexique (golfe du) Résumé : (Auteur) Mangrove wetlands are economically and ecologically important ecosystems and accurate assessment of these wetlands with remote sensing can assist in their management and conservation. This study was conducted to evaluate airborne AISA+ hyperspectral imagery and image transformation and classification techniques for mapping black mangrove populations on the south Texas Gulf coast. AISA+ hyperspectral imagery was acquired from two study sites and both minimum noise fraction (MNF) and inverse MNF transforms were performed. Four classification methods, including minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM), were applied to the noise-reduced hyperspectral imagery and to the band-reduced MNF imagery for distinguishing black mangrove from associated plant species and other cover types. Accuracy assessment showed that overall accuracy varied from 84 percent to 95 percent for site 1 and from 69 percent to 91 percent for site 2 among the eight classifications for each site. The MNF images provided similar or better classification results compared with the hyperspectral images among the four classifiers. Kappa analysis showed that there were no significant differences among the four classifiers with the MNF imagery, though maximum likelihood provided excellent overall and class accuracies for both sites. Producer’s and user’s accuracies for black mangrove were 91 percent and 94 percent, respectively, for site 1 and both 91 percent for site 2 based on maximum likelihood applied to the MNF imagery. These results indicate that airborne hyperspectral imagery combined with image transformation and classification techniques can be a useful tool for monitoring and mapping black mangrove distributions in coastal environments. Copyright ASPRS Numéro de notice : A2009-107 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.75.4.425 En ligne : https://doi.org/10.14358/PERS.75.4.425 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29737 [article]

Use of the Bradley-Terry model to quantify association in remotely sensed images / Alfred Stein in IEEE Transactions on geoscience and remote sensing, vol 43 n° 4 (April 2005)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 43 n° 4 (April 2005) . - pp 852 - 856 Titre : Use of the Bradley-Terry model to quantify association in remotely sensed images Type de document : Article/Communication Auteurs : Alfred Stein, Auteur ; J. Aryal, Auteur ; G. Gort, Auteur Année de publication : 2005 Article en page(s) : pp 852 - 856 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] classification barycentrique [Termes IGN] classification par la distance de Mahalanobis [Termes IGN] classification par maximum de vraisemblance [Termes IGN] estimation de précision [Termes IGN] estimation des paramètres [Termes IGN] image Ikonos [Termes IGN] image Terra-ASTER [Termes IGN] Pays-Bas Résumé : (Auteur) Thematic maps prepared from remotely sensed images require a statistical accuracy assessment. For this purpose, the k-statistic is often used. This statistic does not distinguish between whether one unit is classified as another, or vice versa. In this paper, the Bradley-Terry (BT) model is applied for accuracy assessment. This model compares categories pairwise. The probability of one class over another class is estimated as well as the expected values of class pixels. The study is illustrated with an Advanced Spaceborne Thermal Emission and Reflection Radiometer image from the Netherlands, to which a maximum-likelihood classification with the Euclidean distance is applied. An error matrix is generated using an IKONOS image from the same area as ground truth. It is shown to which degree the BT model extends the K-statistic. A comparison with the Mahalanobis distance is made. Standardization is carried out to overcome problems emerging from the fact that a common BT model does not include the number of correctly classified pixels. The study shows how the BT model serves as an alternative to the usual k-statistic. Numéro de notice : A2005-193 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2005.843569 En ligne : https://doi.org/10.1109/TGRS.2005.843569 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27330 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-05042	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Clustering with obstacles for geographical data mining / V. Estivill-Castro in ISPRS Journal of photogrammetry and remote sensing, vol 59 n° 1-2 (August 2004 - April 2005)  

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Automated change detection for updates of digital map databases / T. Knudsen in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 11 (November 2003)  

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Integration von Form- und Spektralmerkmalen durch künstliche neuronale Netze bei der Satellitenbildklassifizierung / Karl Segl (1996)

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