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Improving the unsupervised mapping of riparian bugweed in commercial forest plantations using hyperspectral data and LiDAR / Kabir Peerbhay in Geocarto international, vol 36 n° 4 ([01/03/2021])  

Public [article]  inGeocarto international > vol 36 n° 4 [01/03/2021] . - pp 465 - 480 Titre : Improving the unsupervised mapping of riparian bugweed in commercial forest plantations using hyperspectral data and LiDAR Type de document : Article/Communication Auteurs : Kabir Peerbhay, Auteur ; Onisimo Mutanga, Auteur ; Romano Lottering, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 465 - 480 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] carte de la végétation [Termes IGN] classification non dirigée [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] détection d'objet [Termes IGN] données lidar [Termes IGN] espèce exotique envahissante [Termes IGN] forêt ripicole [Termes IGN] image AISA+ [Termes IGN] image hyperspectrale [Termes IGN] précision cartographique [Termes IGN] semis de points Résumé : (auteur) Accurate spatial information on the location of invasive alien plants (IAPs) in riparian environments is critical to fulfilling a comprehensive weed management regime. This study aimed to automatically map the occurrence of riparian bugweed (Solanum mauritianum) using airborne AISA Eagle hyperspectral data (393 nm–994 nm) in conjunction with LiDAR derived height. Utilising an unsupervised random forest (RF) classification approach and Anselin local Moran’s I clustering, results indicate that the integration of LiDAR with minimum noise fraction (MNF) produce the best detection rate (DR) of 88%, the lowest false positive rate (FPR) of 7.14% and an overall mapping accuracy of 83% for riparian bugweed. In comparison, utilising the original hyperspectral wavebands with and without LiDAR produced lower DRs and higher FPRs with overall accuracies of 79% and 68% respectively. This research demonstrates the potential of combining spectral information with LiDAR to accurately map IAPs using an automated unsupervised RF anomaly detection framework. Numéro de notice : A2021-163 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1614101 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1614101 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97084 [article]

Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu–Natal, South Africa / Kabir Yunus Peerbhay in ISPRS Journal of photogrammetry and remote sensing, vol 79 (May 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 79 (May 2013) . - pp 19 - 28 Titre : Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu–Natal, South Africa Type de document : Article/Communication Auteurs : Kabir Yunus Peerbhay, Auteur ; Onisimo Mutanga, Auteur ; Riyad Ismail, Auteur Année de publication : 2013 Article en page(s) : pp 19 - 28 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Afrique du sud (état) [Termes IGN] analyse discriminante [Termes IGN] arbre (flore) [Termes IGN] classification dirigée [Termes IGN] espèce végétale [Termes IGN] forêt [Termes IGN] image AISA+ [Termes IGN] image hyperspectrale [Termes IGN] méthode des moindres carrés Résumé : (Auteur) Discriminating commercial tree species using hyperspectral remote sensing techniques is critical in monitoring the spatial distributions and compositions of commercial forests. However, issues related to data dimensionality and multicollinearity limit the successful application of the technology. The aim of this study was to examine the utility of the partial least squares discriminant analysis (PLS-DA) technique in accurately classifying six exotic commercial forest species (Eucalyptus grandis, Eucalyptus nitens, Eucalyptus smithii, Pinus patula, Pinus elliotii and Acacia mearnsii) using airborne AISA Eagle hyperspectral imagery (393–900 nm). Additionally, the variable importance in the projection (VIP) method was used to identify subsets of bands that could successfully discriminate the forest species. Results indicated that the PLS-DA model that used all the AISA Eagle bands (n = 230) produced an overall accuracy of 80.61% and a kappa value of 0.77, with user’s and producer’s accuracies ranging from 50% to 100%. In comparison, incorporating the optimal subset of VIP selected wavebands (n = 78) in the PLS-DA model resulted in an improved overall accuracy of 88.78% and a kappa value of 0.87, with user’s and producer’s accuracies ranging from 70% to 100%. Bands located predominantly within the visible region of the electromagnetic spectrum (393–723 nm) showed the most capability in terms of discriminating between the six commercial forest species. Overall, the research has demonstrated the potential of using PLS-DA for reducing the dimensionality of hyperspectral datasets as well as determining the optimal subset of bands to produce the highest classification accuracies. Numéro de notice : A2013-231 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.01.013 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.01.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32369 [article]

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A vector sift detector for interest point detection in hyperspectral imagery / L. Dorado-Munoz in IEEE Transactions on geoscience and remote sensing, vol 50 n° 11 Tome 1 (November 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 11 Tome 1 (November 2012) . - pp 4521 - 4533 Titre : A vector sift detector for interest point detection in hyperspectral imagery Type de document : Article/Communication Auteurs : L. Dorado-Munoz, Auteur ; M. Velez-Reys, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 4521 - 4533 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] données vectorielles [Termes IGN] extraction automatique [Termes IGN] image AISA+ [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale [Termes IGN] point d'intérêt [Termes IGN] SIFT (algorithme) Résumé : (Auteur) This paper presents an algorithm for the extraction of interest points in hyperspectral images. Interest points are spatial features of the image that capture information from their neighbors, are distinctive and stable under transformations such as translation and rotation, are helpful in data reduction, and reduce the computational burden of various algorithms such as image registration by replacing an exhaustive search over the entire image domain by a probe into a concise set of highly informative points. Interest points have been applied to problems in computer vision, including image matching, recognition, 3-D reconstruction, and change detection. Interest point operators for monochromatic images were proposed more than a decade ago and have extensively been studied. An interest point operator seeks out points in an image that are structurally distinct, invariant to imaging conditions, and stable under geometric transformations. An extension of Lowe's scale-invariant feature transform (SIFT) to vector images is proposed here. The approach takes the vectorial nature of the hyperspectral images into account. Furthermore, the multiscale representation of the image is generated by vector nonlinear diffusion, which leads to improved detection, because it better preserves edges in the image as opposed to Gaussian blurring, which is used in Lowe's original approach. Experiments with hyperspectral images of the same and different resolutions that were collected with the Airborne Hyperspectral Imaging System (AISA) and Hyperion sensors are presented. Evaluation of the proposed approach using repeatability criterion and image registration is carried out. Comparisons with other approaches that were described in the literature are presented. Numéro de notice : A2012-591 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2191791 Date de publication en ligne : 09/05/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2191791 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32037 [article]

Fusion of feature selection and optimized immune networks for hyperspectral image classification of urban landscapes / J. Im in Geocarto international, vol 27 n° 5 (August 2012)  

Public [article]  inGeocarto international > vol 27 n° 5 (August 2012) . - pp 373 - 393 Titre : Fusion of feature selection and optimized immune networks for hyperspectral image classification of urban landscapes Type de document : Article/Communication Auteurs : J. Im, Auteur ; Zhong Lu, Auteur ; J. Rhee, Auteur ; R. Jensen, Auteur Année de publication : 2012 Article en page(s) : pp 373 - 393 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme génétique [Termes IGN] classification par réseau neuronal [Termes IGN] données lidar [Termes IGN] entropie [Termes IGN] image AISA+ [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale [Termes IGN] milieu urbain [Termes IGN] New York (Etats-Unis ; état) Résumé : (Auteur) The urban landscape is dynamic and complex. As improved remote sensing data in terms of spatial and spectral characteristics became available, more sophisticated methods have been adopted for urban applications. This study proposed and evaluated a classification model incorporating feature selection, artificial immune networks and parameter optimization. Information gain, a broadly applied feature selection metric used in data mining techniques such as decision trees, was used for feature selection. Two types of information gain – binary-class entropy and multiple-class entropy – were investigated. Artificial immune networks have been recently applied to remote sensing classification and have been proven useful especially when multiple parameters of the networks are optimized through a genetic algorithm. The proposed model was tested for urban classification using hyperspectral (i.e. AISA and Hyperion) and LiDAR data over two urban study sites. Results show that the model considerably reduced processing time (70%) for classification without significant accuracy decrease. Numéro de notice : A2012-369 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2011.642898 Date de publication en ligne : 06/01/2012 En ligne : https://doi.org/10.1080/10106049.2011.642898 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31815 [article]

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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]

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Spectral analysis of coastal vegetation and land cover using AISA+ hyperspectral data / R. Jensen in Geocarto international, vol 22 n° 1 (March - May 2007)  

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