Remote Sensing of Environment, 44, 145–163. The spectral image processing system (sips) – interactive visualization and analysis of imaging spectrometer data. Petropoulos, Krishna Prasad Vadrevu & Chariton Kalaitzidis (2012): Spectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region, Geocarto International, DOI:10.1080/10106049.2012.668950 1 Issue 4 īertels Luc, Bart Deronde, Pieter Kempeneers, Walter Debruyn, Sam Provoost (2005) Optimized Spectral Angle Mapper classification of spatially heterogeneous dynamic dune vegetation, a case study along the Belgian coastiline The 9 th international Symposium on Physical Measurements and Signatures in Remote Sensing (ISPMSRS). Rashmi S, Swapna Addamani, Venkat and Ravikiran S (2014) Spectral Angle Mapper Algorithm for Remote Sensing Image Classification ISSN 2348 – 7968 Vol. Libourel (2016) Formalisation des chaines de traitements de données spatiales satellitaires sur la forêt à Madagascar 289-301 Īimé Richard Hajalalaina, Dominique Hervé, J. Salim CHITROUB (2004) Combinaison de classiffeurs:une approche pour l’amelioration de la classification d’images multisources/Multidates de teledetection Télédétection, vol. Marine Campedel, Eric Moulines (2014) Méthodologie de sélection de caractéristiques pour la classification d’images satellitaires Nadia OUARAB, Youcef SMARA, Jean-Paul RASSON (1999) Utilisation de methods de classification hierarchique pour une classification supervisée d’image Dix-septième colloque GRETSI, Vannes Carriere, Dominique Hervé (2015) Détection de changement de l’occupation du sol dans une commune à la peripherie de la forêt humide de Fianarantsoa Based on these results, the minimum distance showed a higher accuracy and gave us 13462.1842 ha of forest area, 16798.8006 ha of prairie for the year 2018.Īimé Richard Hajalalaina, Manuel Grizonnet, Eric Delaitre, Solofo Rakotondraompiana, Dominique Hervé (2013) Discrimination des zones humides en foret Malgache, proposition d’une methodologie multiresolution et multisource utilisant orfeo toolbox Revue de Photogrammetrie et de télédétection.Īvisoatolona Andrianarivo, Eric Delaitre, Anne Elisabeth Laques, Stéphanie. After repeating the classification several times, we obtained accuracies of 77%, 75%, 88%, 84% and 90% with Kappa indices of 0.64, 0.61, 0.80, 0.76 and 0.84 for the Spectral Angle Mapper, Spectral Correlation Mapper, Maximum Likelihood, Mahalanobis Distance and Minimum Distance. The validation of the classification results was performed using several reference points, a previous national processing result already validated in the field and the Google earth image of the same year. Then, a comparison study of the supervised classification algorithms was done to obtain a more accurate result. The processing was moved to spectral preparation and improvement of spatial resolution using the blue, green, red, near infrared and panchromatic channels. For this, we adopted the methodology of satellite image processing based on supervised classification algorithms. This paper focuses on the Landsat 8 satellite image classification of the OLI sensor via the remote sensing software Erdas Imagine in order to calculate the land cover surface and to establish the mapping of the special reserve Kalambatritra of Madagascar for the year 2018.
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