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Mapping the distribution of ferric iron minerals on a vertical mine face using derivative analysis of hyperspectral imagery (430–970 nm) / R. Murphy in ISPRS Journal of photogrammetry and remote sensing, vol 75 (January 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 75 (January 2013) . - pp 29 - 39 Titre : Mapping the distribution of ferric iron minerals on a vertical mine face using derivative analysis of hyperspectral imagery (430–970 nm) Type de document : Article/Communication Auteurs : R. Murphy, Auteur ; S. Monteiro, Auteur Année de publication : 2013 Article en page(s) : pp 29 - 39 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Advanced Visible & Near Infrared Radiometer [Termes IGN] analyse d'image numérique [Termes IGN] Australie occidentale (Australie) [Termes IGN] dérivée [Termes IGN] image hyperspectrale [Termes IGN] mine de fer [Termes IGN] photographie infrarouge couleur [Termes IGN] rayonnement proche infrarouge [Termes IGN] réflectance spectrale Résumé : (Auteur) Hyperspectral imagery is used to map the distribution of iron and separate iron ore from shale (a waste product) on a vertical mine face in an open-pit mine in the Pilbara, Western Australia. Vertical mine faces have complex surface geometries which cause large spatial variations in the amount of incident and reflected light. Methods used to analyse imagery must minimise these effects whilst preserving any spectral variations between rock types and minerals. Derivative analysis of spectra to the 1st-, 2nd- and 4th-order is used to do this. To quantify the relative amounts and distribution of iron, the derivative spectrum is integrated across the visible and near infrared spectral range (430–970 nm) and over those wavelength regions containing individual peaks and troughs associated with specific iron absorption features. As a test of this methodology, results from laboratory spectra acquired from representative rock samples were compared with total amounts of iron minerals from X-ray diffraction (XRD) analysis. Relationships between derivatives integrated over the visible near-infrared range and total amounts (% weight) of iron minerals were strongest for the 4th- and 2nd-derivative (R2 = 0.77 and 0.74, respectively) and weakest for the 1st-derivative (R2 = 0.56). Integrated values of individual peaks and troughs showed moderate to strong relationships in 2nd- (R2 = 0.68–0.78) and 4th-derivative (R2 = 0.49–0.78) spectra. The weakest relationships were found for peaks or troughs towards longer wavelengths. The same derivative methods were then applied to imagery to quantify relative amounts of iron minerals on a mine face. Before analyses, predictions were made about the relative abundances of iron in the different geological zones on the mine face, as mapped from field surveys. Integration of the whole spectral curve (430–970 nm) from the 2nd- and 4th-derivative gave results which were entirely consistent with predictions. Conversely, integration of the 1st-derivative gave results that did not fit with predictions nor distinguish between zones with very large and small amounts of iron oxide. Classified maps of ore and shale were created using a simple level-slice of the 1st-derivative reflectance at 702, 765 and 809 nm. Pixels classified as shale showed a similar distribution to kaolinite (an indicator of shales in the region), as mapped by the depth of the diagnostic kaolinite absorption feature at 2196 nm. Standard statistical measures of classification performance (accuracy, precision, recall and the Kappa coefficient of agreement) indicated that nearly all of the pixels were classified correctly using 1st-derivative reflectance at 765 and 809 nm. These results indicate that data from the VNIR (430–970 nm) can be used to quantify, without a priori knowledge, the total amount of iron minerals and to distinguish ore from shale on vertical mine faces. Numéro de notice : A2013-030 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.09.014 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.09.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32168 [article]

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081-2013011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Evaluating classification techniques for mapping vertical geology using field-based hyperspectral sensors / R.J. Murphy in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 8 (August 2012) . - pp 3066 - 3080 Titre : Evaluating classification techniques for mapping vertical geology using field-based hyperspectral sensors Type de document : Article/Communication Auteurs : R.J. Murphy, Auteur ; S. Monteiro, Auteur ; S. Schneider, Auteur Année de publication : 2012 Article en page(s) : pp 3066 - 3080 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Australie occidentale (Australie) [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification Spectral angle mapper [Termes IGN] image aérienne [Termes IGN] image hyperspectrale [Termes IGN] mine [Termes IGN] ombre Résumé : (Auteur) Hyperspectral data acquired from field-based platforms present new challenges for their analysis, particularly for complex vertical surfaces exposed to large changes in the geometry and intensity of illumination. The use of hyperspectral data to map rock types on a vertical mine face is demonstrated, with a view to providing real-time information for automated mining applications. The performance of two classification techniques, namely, spectral angle mapper (SAM) and support vector machines (SVMs), is compared rigorously using a spectral library acquired under various conditions of illumination. SAM and SVM are then applied to a mine face, and results are compared with geological boundaries mapped in the field. Effects of changing conditions of illumination, including shadow, were investigated by applying SAM and SVM to imagery acquired at different times of the day. As expected, classification of the spectral libraries showed that, on average, SVM gave superior results for SAM, although SAM performed better where spectra were acquired under conditions of shadow. In contrast, when applied to hypserspectral imagery of a mine face, SVM did not perform as well as SAM. Shadow, through its impact upon spectral curve shape and albedo, had a profound impact on classification using SAM and SVM. Numéro de notice : A2012-381 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2178419 Date de publication en ligne : 03/02/2012 En ligne : https://doi.org/10.1109/TGRS.2011.2178419 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31827 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2012081	RAB	Revue	Centre de documentation	En réserve L003	Disponible