Supervised Classification

Supervised classification is a widely used method for producing thematic maps from remote sensingRemote SensingRemote sensing is the science of collecting data about Earth's surface without direct physical contact, primarily usi... imagery. The analyst defines the target classes (such as water, forest, urban, and cropland), collects representative training samples for each class, and then trains a classification algorithm to assign every pixel or object in the image to one of those classes based on its spectral, spatial, or textural characteristics. Common algorithmsPopular supervised classifiers include maximum likelihood (a parametric statistical method), random forestRandom ForestRandom Forest is an ensemble machine learning method that builds multiple decision trees during training and merges t... and gradient boostingGradient BoostingGradient Boosting is a sequential ensemble learning technique that builds models iteratively, with each new model cor... (ensemble decision-tree methods), support vector machines (SVM), and deep learning architectures such as convolutional neural networks. Each offers different trade-offs between accuracy, computational cost, and sensitivity to training sample size and quality. Workflow and accuracy assessmentThe supervised classification workflow involves class definition, training sample collection, feature selection, algorithm training, full-image classificationImage ClassificationImage classification is the process of categorizing pixels in remote sensing imagery into land cover or land use clas..., and accuracy assessment using an independent validation dataset. Accuracy metrics such as overall accuracy, producer's and user's accuracy, and the kappa coefficient quantify how well the classified map matches ground truthGround TruthGround truth refers to data collected at the Earth's surface to validate and calibrate information derived from remot.... Supervised classification remains the dominant approach for producing high-quality land cover maps from remote sensing data. As machine learning methods continue to advance, supervised techniques are becoming more accurate, efficient, and accessible for operational mapping programs.