Object based classifcation

We are also increasingly interested in land use. However, to distinguish, for example, urban from rural woodland, or a swimming pool from a natural pond, an approach similar to visual interpretation is needed. Object-oriented analysis (OOA), also called segmentation-based analysis, allows us to do that. Instead of trying to classify every pixel separately, and only based on spectral information,

OOA breaks down an image into spectrally homogeneous segments that correspond to fields, tree stands, buildings, etc. It is also possible to use auxiliary GIS layers, for example building footprints, to guide this segmentation. Similarly to the cognitive approach of visual image interpretation—where we consider each element in terms of its spectral appearance but also in terms of its shape and texture, and within its environment—in OOA we can then specify contextual relationships and more complex segment characteristics to classify the objects extracted in the segmentation process. For example, we can use object texture to distinguish two spectrally similar forest types, or distinguish a swimming pool from a pond, by considering its shape and perhaps the surrounding concrete instead of soil and vegetation. OOA is particularly suitable for images of high spatial resolution, but also for data obtained by ALS or microwave radar. It requires that we have substantial knowledge on what distinguishes a given land cover or land use type, as well as auxiliary data such as elevation, soil type or vector layers.