Evaluating land cover types from Landsat TM using SAGA GIS for vegetation mapping based on ISODATA and K-means clustering
Résumé
The paper presents the cartographic processing of the Landsat TM image by the two unsupervised classification methods of SAGA GIS: ISODATA and K-means clustering. The approaches were tested and compared for land cover type mapping. Vegetation areas were detected and separated from other land cover types in the study area of southwestern Iceland. The number of clusters was set to ten classes. The processing of the satellite image by SAGA GIS was achieved using Imagery Classification tools in the Geoprocessing menu of SAGA GIS. Unsupervised classification performed effectively in the unlabeled pixels for the land cover types using machine learning in GIS. Following an iterative approach of clustering, the pixels were grouped in each step of the algorithm and the clusters were reassigned as centroids. The paper contributes to the technical development of the application of machine learning in cartography by demonstrating the effectiveness of SAGA GIS in remote sensing data processing applied for vegetation and environmental mapping.
Domaines
Informatique [cs] Sciences de l'environnement Recherche d'information [cs.IR] Vision par ordinateur et reconnaissance de formes [cs.CV] Traitement des images [eess.IV] Milieux et Changements globaux Ingénierie de l'environnement Biodiversité et Ecologie Sciences de la Terre Biodiversité Ecologie, Environnement Synthèse d'image et réalité virtuelle [cs.GR] Ingénierie assistée par ordinateurOrigine | Fichiers éditeurs autorisés sur une archive ouverte |
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