Open AccessEvaluación de parámetros de segmentación en OBIA para la clasificación de coberturas del suelo a partir de imágenes VANT
Author(s) -
Susana I. Hinojosa-Espinoza,
José Luis Gallardo-Salazar,
Félix J. C. Hinojosa-Espinoza,
Anulfo Meléndez-Soto
Publication year - 2021
Publication title -
revista de teledetección
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.271
H-Index - 9
eISSN - 1988-8740
pISSN - 1133-0953
DOI - 10.4995/raet.2021.14782
Subject(s) - segmentation , computer science , artificial intelligence , image segmentation , pixel , random forest , mathematics , pattern recognition (psychology) , cartography , geography
Unmanned Aerial Vehicles (UAVs) have given a new boost to remote sensing and image classification techniques due to the high level of detail among other factors. Object-based image analysis (OBIA) could improve classification accuracy unlike to pixel-based, especially in high-resolution images. OBIA application for image classification consists of three stages i.e., segmentation, class definition and training polygons, and classification. However, defining the parameters: spatial radius (SR), range radius (RR) and minimum region size (MR) is necessary during the segmentation stage. Despite their relevance, they are usually visually adjusted, which leads to a subjective interpretation. Therefore, it is of utmost importance to generate knowledge focused on evaluating combinations of these parameters. This study describes the use of the mean-shift segmentation algorithm followed by Random Forest classifier using Orfeo Toolbox software. It was considered a multispectral orthomosaic derived from UAV to generate a suburban map land cover in town of El Pueblito, Durango, Mexico. The main aim was to evaluate efficiency and segmentation quality of nine parameter combinations previously reported in scientific studies.This in terms of number generated polygons, processing time, discrepancy measures for segmentation and classification accuracy metrics. Results evidenced the importance of calibrating the input parameters in the segmentation algorithms. Best combination was RE=5, RR=7 and TMR=250, with a Kappa index of 0.90 and shortest processing time. On the other hand, RR showed a strong and inversely proportional degree of association regarding the classification accuracy metrics.