ABSTRACT
A problem of major interest to regional planning organizations, disaster relief
agencies, and the military is the identification and tracking of land development
across large scale regions, and over time. We develop an autonomous image analysis
system to understand land development, especially residential and urban building
organizations from satellite images.
We introduce a set of measures based on straight lines to assess land development
levels in high resolution satellite images. Urban areas exhibit a preponderance of
straight line features. Rural areas produce line structures in more random spatial
arrangements. We use this observation to perform an initial triage on the image to
restrict the attention of subsequent, more computationally intensive analyses.
Vegetation indices have been used extensively to estimate the vegetation density
from satellite and airborne images for many years. We use these as the multispectral
information for classification and house and road extraction. We focus on the nor-
malized difference vegetation index (NDVI ) and introduce a statistical framework to
analyze and extend it. Using the established statistical framework, we introduce new
a group of shadow-water indices.
We then extend our straight line based measures by developing a synergistic ap-
proach that combines structural and multispectral information. In particular, the
structural features serve as cue regions for multispectral features.
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