Object-based image analysis

Object-based image analysis (OBIA) involves pixels first being grouped into objects based on either spectral similarity or an external variable such as ownership, soil or geological unit. Each object is also part of a 'super-object,' obtained by combining several neighboring objects into one larger, and each can be subdivided into smaller objects called sub-objects. OBIA can also offer reliable products where traditional image analysis fails completely. Another example of innovative use of OBIA is in land-use classification. Land-use describes the function of an area, whereas land-cover describes its physical appearance. There is no unique relationship between land-use and land-cover, so several land-cover classes may correspond to a single land-use class and vice versa. OBIA offers a solution by identifying tree groups as image-objects and deducing the number of trees from textural characteristics. Stand density is no longer based on individual tree identification, but relies on small-area statistics characterizing local texture.