Object-based image analysis and machine learning for mapping cashew plantations in Ariyalur district, Tamil Nadu
DOI:
https://doi.org/10.14719/pst.5165Keywords:
image segmentation, machine learning, OBIA, rule set, sentinel 2 MSIAbstract
An object-based image analysis (OBIA) approach provides a comprehensive method for delineating homogeneous segments based on spectral charac-teristics, geometry, and spatial imagery structures. The present study utiliz-es OBIA and machine learning (ML) techniques to map cashew plantations in Ariyalur district of Tamil Nadu, India. Sentinel-2 Multi-Spectral Instrument (MSI) imagery, acquired during the 2023 kharif season, was employed as the primary data source due to its high spatial and spectral resolution, suitable for detailed land cover mapping. The OBIA methodology involved multi-resolution segmentation using eCognition software to delineate homogene-ous image objects based on spectral, spatial, and contextual characteristics. Machine learning algorithms, including random forest (RF), support vector machine (SVM), and decision tree (DT), were evaluated to improve classifi-cation accuracy. The SVM demonstrated the best superior performance, achieving an overall accuracy of 92.1% and a kappa coefficient of 0.85. The results underscore the effectiveness of ML techniques in conjunction with OBIA for precise cashew plantation mapping while contributing to improved land use/land cover mapping, agricultural resource management, and sus-tainable development within the region.
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