Detection of Compound Structures Using a Gaussian Mixture Model With Spectral and Spatial Constraints

Increasing spectral and spatial resolution of new-generation remotely sensed images necessitate the joint use of both types of information for detection and classification tasks. This paper describes a new approach for detecting heterogeneous compound structures such as different types of residential, agricultural, commercial, and industrial areas that are comprised of spatial arrangements of primitive objects such as buildings, roads, and trees. The proposed approach uses Gaussian mixture models (GMMs), in which the individual Gaussian components model the spectral and shape characteristics of the individual primitives and an associated layout model is used to model their spatial arrangements. We propose a novel expectation-maximization (EM) algorithm that solves the detection problem using constrained optimization. The input is an example structure of interest that is used to estimate a reference GMM and construct spectral and spatial constraints. Then, the EM algorithm fits a new GMM to the target image data so that the pixels with high likelihoods of being similar to the Gaussian object models while satisfying the spatial layout constraints are identified without any requirement for region segmentation. Experiments using WorldView-2 images show that the proposed method can detect high-level structures that cannot be modeled using traditional techniques.