Modeling Spatial Dependencies in High-Resolution Overhead Imagery

Abstract

Human settlement regions with different physical and socio-economic attributes exhibit unique spatial characteristics that are often illustrated in high-resolution overhead imageries. For example- size, shape and spatial arrangements of man-made structures are key attributes that vary with respect to the socioeconomic profile of the neighborhood. Successfully modeling these attributes is crucial in developing advanced image understanding systems for interpreting complex aerial scenes. In this paper we present three different approaches to model the spatial context in the overhead imagery. First, we show that the frequency domain of the image can be used to model the spatial context [1]. The shape of the spectral energy contours characterize the scene context and can be exploited as global features. Secondly, we explore a discriminative framework based on the Conditional Random Fields (CRF) [2] to model the spatial context in the overhead imagery. The features derived from the edge orientation distribution calculated for a neighborhood and the associated class labels are used as input features to model the spatial context. Our third approach is based on grouping spatially connected pixels based on the low-level edge primitives to form support-regions [3]. The statistical parameters generated from the support-region feature distributions characterize different geospatial neighborhoods. We apply our approaches on high-resolution overhead imageries. We show that proposed approaches characterize the spatial context in overhead imageries.