Object-based image analysis (OBIA) is one of several approaches developed to overcome the limitations of the pixel-based approaches. It incorporates spectral, textural and contextual information to identify thematic classes in an image. The first step in OBIA is to segment the image into homogeneous objects. The term object here stands for a contiguous cluster of pixels. Segmentation is based on pre-defined parameters like compactness, shape, and scale, derived from real-world knowledge of the features that one wants to identify (Mason et al. 1988). In crop mapping, for instance, this will require understanding of size and shape of farm fields in the area of interest. Over- and under-segmentation are obvious threats to these approaches, and need to be addressed, and not all semantically meaningful entities may be identified.
In a second step, each object (segment) is classified on the basis of one or more statistical properties of the contained pixels. This means that all pixels within a segment are assigned to one class, eliminating the within-field spectral variability and mixed pixels problems associated with pixel-based approaches. Several studies have confirmed the superiority of OBIA over pixel-based classifications, especially in heterogeneous agricultural landscapes and urban areas (Blaschke, 2010; Myint, 2006; Myint et al., 2011; Peña-Barragán et al., 2011).