Multiclass and Binary SVM Classification: Implications for Training and Classification Users

By Ajay Mathur, Giles M. Foody. IEEE Geoscience and Remote Sensing Letters, 2008

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Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations - A Review (2020, methodology): “For simulated and real-world hyperspectral satellite data, the genetically optimized SVM using the support vectors shows the best performance [79].”
Vision-Language Models for Vision Tasks: A Survey (2023, background): “Before the deep learning era [4], visual recognition studies rely heavily on feature engineering with hand-crafted features [9], [38] and lightweight learning models [7], [8], [39] that classify the hand-crafted features into pre-defined semantic categories.”
Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers (2014, methodology): “…on SVM theory and mathematical formulation, the reader is directed to introductory texts by Burges (1998), Cortes and Vapnik (1995), Mathur and Foody (2008), and Karatzoglou, Meyer, and Hornik (2006) In this study, RapidEye spectral bands ( N = 5) were used for defining the space feature of…”
Support vector machines in remote sensing: A review (2011, neutral): “Mathur and Foody (2008a) evaluated the performance of SVMs in non-binary classification tasks.”