Supervised object-based image classification process

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In this YouTube video, we'll explore the crucial process of collecting training samples for image classification. Training samples can take the form of points, polygons, or segments, depending on the represented class, and it's essential to gather samples for all the classes you aim to classify in your final raster. Adequate sample sizes are crucial to effectively represent the features of interest and ensure robust statistical comparisons. Inadequate training samples can distort classification results. We'll also delve into the distinction between pixel-based and object-based training samples. To illustrate this concept, imagine you're a GIS analyst for an emergency management team tasked with classifying wildfire damage from segmented raster data. We'll show you how to use the Training Samples Manager and create an Esri classifier definition file to streamline the supervised object-based image classification process.