A classification approach to finding buildings in large scale aerial photographs

Automatic building extraction remains an open research problem in digital photogrammetry. While many algorithms are proposed for building extraction, none of these solve the problem completely. One of their limitations is in the initial detection of the presence or absence of a building in the image region. One approach to the initial detection of buildings is to cast the problem as one of classification, where the image is divided into patches that either contain or do not contain a building. Support Vector Machines (SVMs) are a relatively new classification tool that appear well suited to this task. They are closely related to other machine learning techniques such as neural networks but have a stronger base in statistical theory and produce a generalised solution to the classification problem, using the principles of structural risk minimisation.