Support Vector Machine (SVM)

A Support Vector Machine is a supervised machine learning algorithm that finds the decision boundary, or hyperplane, that maximizes the margin between different classes in feature space. By maximizing this margin, SVMs achieve strong generalizationGeneralizationGeneralization is the process of simplifying geographic features and reducing detail in spatial data to create maps a... to unseen data. The algorithm identifies a subset of training points closest to the boundary, called support vectors, which uniquely define the optimal hyperplane. For data that is not linearly separable, SVMs use kernel functions to project inputs into higher-dimensional spaces where linear separation becomes possible. Geospatial Classification with SVMsSVMs have been extensively used in remote sensingRemote SensingRemote sensing is the science of collecting data about Earth's surface without direct physical contact, primarily usi... for land cover classificationLand Cover ClassificationLand cover classification is the process of categorizing Earth's surface into distinct classes such as forest, cropla..., where each pixel is represented by a feature vector of spectral bandSpectral BandA spectral band is a specific range of wavelengths within the electromagnetic spectrum captured by a remote sensing s... values. The kernel trick allows SVMs to model complex, nonlinear class boundaries that arise from spectral overlap between land cover types like different crop species or urban surfaces. SVMs perform well with relatively small training samples, a common constraint in geospatial projects where labeled data requires expert interpretation. They are effective for hyperspectral image classificationImage ClassificationImage classification is the process of categorizing pixels in remote sensing imagery into land cover or land use clas... where feature dimensionality is very high, a scenario where many other algorithms struggle without extensive dimensionality reduction. Strengths and Limitations for Spatial AnalysisSVMs are memory-efficient because they depend only on support vectors rather than the full training set, and they resist overfitting in high-dimensional spaces. However, they scale poorly to very large datasets common in modern satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes..., with training time growing quadratically or worse with sample size. SVMs lack the probabilistic output and feature importance measures that tree-based methods provide, and kernel selection and parameter tuning require careful experimentation. For large-scale geospatial tasks, SVMs have been largely superseded by ensemble methodsEnsemble MethodsEnsemble Methods combine multiple machine learning models to produce predictions that are more accurate and robust th... and deep learning, though they remain valuable for smaller specialized classification problems.