Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data - Computational Approaches for Urban Environments - page 238

vision, medical imaging, remote sensing, and robotics applications, and this is the
reason why image registration, segmentation, and object detection hold the biggest
share in modern image analysis and computer vision research and development
(Sotiras et al. 2013 ).
Speaking briefly, the image registration task involves three main components:
a transformation model, an objective function, and an optimization method.
The success of the procedure depends naturally on the transformation model and the
objective function. The dependency on the optimization process follows from the
fact that image registration is inherently an ill-posed problem. Actually, in almost
all realistic scenarios and computer vision applications, the registration is ill-posed
according to Hadamard's definition of well-posed problems. Therefore, devising
each component of the registration algorithm in such way that the requirements
(regarding accuracy, automation, speed, etc. ) are met is a demanding and
challenging process (Eastman et al. 2007 ; Le Moigne et al. 2011 ; Sotiras et al. 2013 ).
The intensive research on invariant feature descriptors (Lowe 2004 ) empowered
the automation in the feature detection (points, lines, regions, templates, etc. )
procedure. Along with the model fitting approaches, through iterative non-
deterministic algorithms, an optimal set of the selected mathematical model
parameters ( i.e., transformation, deformation, etc. ) is detected excluding outliers.
Area-based methods, mutual information methods, and descriptor-based algorithms
restore data deformations and through a resampling data are warped to the reference.
Furthermore, since the effective modeling requires rich spatial, spectral, and
temporal observations over the structured environment recent approaches fuse data
from various sensors, i.e., multimodal data (Fig. 10.1 ). The various sensors include
frame and push-broom cameras and multispectral, hyperspectral, and thermal
cameras, while the various platforms include satellite, airborne, UAV, and ground
systems.
In multimodal data registration (De Nigris et al. 2012 ;Heetal. 2011b ), mutual
information techniques have become a standard reference, mainly in medical imag-
ing (Legg et al. 2013 ; Wachinger and Navab 2012 ; Sotiras et al. 2013 ). However,
being an area-based technique, the mutual information process possesses natural
limitations. To address them, a combination with other, preferably feature-based,
methods have gain high robustness and reliability. To speed up the computation,
scale space representations (Tzotsos et al. 2014 ) are employed along with fast op-
timization algorithms. However, when data have significant rotation and/or scaling
differences, these methods either fail or become extremely time expensive. Future
development on addressing the multimodal data challenges may concentrate more
on feature-based methods, where appropriate invariant and modality-insensitive
features (Heinrich et al. 2012 ) can provide the reliable and adequate volume of
features for a generic and automated multimodal data registration.
To sum up, the described radiometric and geometric corrections between all the
available data of a given time series transform raw data to valuable “ready-for-
analysis” geospatial datasets and ensure an optimal exploitation from the following,
in the processing chain, core change detection algorithms.