Updated: Aug 6, 2021
Object-based or object-oriented classification uses both spectral and spatial information for classification. The process involves categorization of pixels based on their spectral characteristics, shape, texture and spatial relationship with the surrounding pixels. Object-based classification methods were developed relatively recently compared to traditional pixel based classification techniques.
While pixel based classification is based solely on the spectral information in each pixel, object-based classification is based on information from a set of similar pixels called objects or image objects. Image objects or features are groups of pixels that are similar to one another based on the spectral properties (i.e., color), size, shape, and texture, as well as context from a neighborhood surrounding the pixels.
Object-based classification is a two step process, first the image is segmented or broken into discrete objects or features with and then each object is classified. This type of classification attempts to mimic the type of analysis done by humans during visual interpretation.