Detection of compound structures using a Gaussian mixture model with spectral and spatial constraints

High spectral and high spatial resolution images acquired from new generation satellites have enabled new applications. However, the increasing amount of detail in these images also necessitates new algorithms for automatic analysis. This paper describes a new approach to discover compound structures such as different types of residential, commercial, and industrial areas that are comprised of spatial arrangements of primitive objects such as buildings, roads, and trees. The proposed approach uses a robust Gaussian mixture model (GMM) where each Gaussian component models the spectral and shape content of a group of pixels corresponding to a primitive object. The algorithm can also incorporate spatial constraints on the layout of the primitive objects in terms of their relative positions. Given example structures of interest, a new learning algorithm fits a GMM to the image data, and this model can be used to detect other similar structures by grouping pixels that have high likelihoods of belonging to the Gaussian object models while satisfying the spatial layout constraints without any requirement for region segmentation. Experiments using WorldView-2 data show that the proposed method can detect high-level structures that cannot be modeled using traditional techniques.