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.
