Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data - Computational Approaches for Urban Environments - page 237

atmospheric corrections does not guarantee the multitemporal homogeneity of
( e.g., Landsat) datasets since complete atmospheric properties are difficult to
quantify and simplifications are commonly assumed (Han et al. 2007 ). There-
fore, a cross-calibration between the data stack and time series can address the
problem.
Given a remote sensing optical dataset, the first step is to convert the capture
radiance, the raw digital numbers to the “top of atmosphere” values (Chander et
al. 2009 ; Villa et al. 2012 , and the references therein). Then the second step is
to model the upward and downward irradiance which is constrained by the gases
absorption and the water molecules and aerosols scattering. Complex radiative
transference models simulate the atmosphere and light interactions between the
sun-to-terrain and terrain-to-sensor trajectories. Although, such an atmospheric
correction can account for signal attenuation and restore in some extent the
intercomparability of satellite images taken on different dates, “top of atmosphere”
values are widely used directly for inventory and ecosystem studies or in procedures
that are based on post-classification change detection approaches. However, recent
studies indicate that cross-calibration and atmospheric corrections are required
prior to relative normalization since certain remote sensing products and accurate
biophysical parameters like vegetation indices cannot be calculated (Vicente-
Serrano et al. 2008 ).
The third step is to model the modified illumination conditions due to the scene
topography. In order to simplify this extremely complex setting, in practice one
concentrates on the shaded areas which deliver less than expected reflectance and
on the sunny areas which deliver more than expected. Then, usually, we assume a
Lambertian terrain behavior or model non-Lambertian effects. Last but not least, a
relative radiometric normalization should be performed between the images of the
time series/dataset, in case where an absolute physical correction model was not
employed. The normalization process is based on a linear comparison between the
images which have been acquired on different dates. To this end, linear regression
or other automated techniques like the pseudo-invariant feature regression has given
promising results (Vicente-Serrano et al. 2008 ) while indicating that the relative
radiometric normalization is an absolutely essential step to ensure high levels of
homogeneity between the images of the dataset.

10.4.2 Geometric Corrections and Data Registration

Once the radiometric and atmospheric calibration has been performed, the next
step is to register, co-register, and geo-reference the available data. Early studies
(Dai and Khorram 1998 ;Roy 2000 ;Bovoloetal. 2009 ) have underlined the
important problems which occurred from data misregistration and how significantly
the change detection product is affected. Therefore, in order to develop operational
detection systems, the registration problem must be addressed with an optimal way
(Klaric et al. 2013 ). In particular, this is a common challenge in most computer