Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data - Computational Approaches for Urban Environments - page 253

10.8.6 The Importance of Operational Data Preprocessing

Most standard remote sensing algorithms and techniques (classifications, indices,
biophysical parameters, model inversions, object detection, etc. ) assume cloud-free
data, already radiometric, atmospheric, and geometric corrected. However, this is
not an operationally solved problem yet. The production of a European cloud-free
mosaic, two times per year, was not 100 % feasible despite the availability of
three different satellite sensors and a considerable flexibility in the date windows
around every region (Hoersch and Amans 2012 ). Moreover, in accordance with
recent relevant studies (Vicente-Serrano et al. 2008 ), this survey underlines the fact
that it is essential to accurately ensure the homogeneity of multitemporal datasets
through operational radiometric and geometric data corrections including sensor
calibration, cross-calibration, atmospheric, geometric, and topographic corrections
and relative radiometric normalization using objective statistical techniques. Be-
ing able to address for the same invariant terrain object, the pictured different
spectral signatures in time series data, being able to construct operationally cloud-
free reflectance surfaces (Villa et al. 2012 ), will further boost the effectiveness
and applicability of remote sensing methods in emerging urban environmental
applications.
To sum up, the significant research interest on urban change detection and
modeling is driven from real, critical, and current environmental and engineering
problems, which pose emerging technological questions and challenges. Recent
advances on the domain indicate that remote sensing and computer vision state-
of-the-art approaches can be fused and further expanded towards the fruitful and
comprehensive exploitation of open, big geospatial data.

Acknowledgment This research has been co-financed by the European Union (European Social
Fund - ESF) and Greek national funds through the Operational Program “Education and
Lifelong Learning” of the National Strategic Reference Framework (NSRF) - Research Funding
Program: THALES: Reinforcement of the interdisciplinary and/or interinstitutional research and
innovation.

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