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@article{182471,
        author = {P Naveen and P N S S Manohara Sarma and Dr. M. Senthil Kumaran},
        title = {Harnessing the Power of Machine Learning for SatelliteBased Land Use and Land Cover Classification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {2568-2573},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182471},
        abstract = {The accurate classification of Land Use and Land Cover (LU/LC) is essential for environmental monitoring, urban planning, agriculture, and disaster response. Traditional classification methods, such as pixel-based and statistical approaches, often struggle to process high-dimensional satellite imagery effectively, leading to inconsistencies and reduced accuracy. Machine learning (ML) techniques provide a robust alternative, enabling automation, improved classification precision, and the ability to handle complex, multi-dimensional data.
This study conducts a comprehensive comparison of ML algorithms, including Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), to determine their efficiency in classifying high-resolution Optical Land Imager (OLI) satellite data. Key performance metrics—Overall Accuracy, Kappa Coefficient, and F1-score—are employed to evaluate classification effectiveness. Our findings suggest that SVM with the Radial Basis Function (RBF) kernel provides strong classification capabilities, particularly in distinguishing non-linearly separable data, while CNNs demonstrate superior feature extraction and pattern recognition.},
        keywords = {Machine Learning, Support Vector Machines, Land Use, Land Cover, Remote Sensing, Convolutional Neural Networks, Deep Learning, Ensemble Learning.},
        month = {July},
        }