Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data - Computational Approaches for Urban Environments - page 241

An important number of approaches are based on ratios, kernels, change vector
analysis, and indices (Bovolo et al. 2012 ; Canty and Nielsen 2008 ; Celik 2009 ;Chen
et al. 2011 ; Dalla Mura et al. 2008 ; Renza et al. 2013 ; Demir et al. 2013 ; Gueguen
et al. 2011 ; Marchesi and Bruzzone 2009 ; Marpu et al. 2011 ; Volpi et al. 2012 ).
Other efforts are based on multiscale analysis like wavelets (Bovolo et al. 2013 ;
Celik and Ma 2010 , 2011 ; Dalla Mura et al. 2008 ; Moser et al. 2011 ), fuzzy theory
(Ling et al. 2011 ;LuoandLi 2011 ; Robin et al. 2010 ), clustering and MRFs (Aiazzi
et al. 2013 ; Celik 2010 ; Ghosh et al. 2011 , 2013 ;Salmonetal. 2011 ; Moser and
Serpico 2009 ; Moser et al. 2011 ;Wangetal. 2013 ).
Spectral mixture analysis (Yetgin 2012 ), level sets (Bazi et al. 2010 ; Hao et al.
2014 ), and data fusion approaches (Du et al. 2012 , 2013 ; Moser and Serpico 2009 ;
Ma et al. 2012 ; Gong et al. 2012 ) are holding an important share also. Moreover, and
despite the fact that their core employed algorithms are supervised, recent proposed
automated studies are based on object-based techniques (Bouziani et al. 2010 ),
semi-supervised support vectors (Bovolo et al. 2008 ), and neural networks (Pacifici
and Del Frate 2010 ).
In addition, among the recent unsupervised techniques, a clear computational
advantage possess the ones who can address the dependence between spatially adja-
cent image neighbors either by standard texture or morphological measures or either
by clustering, Markov random fields, Bayesian networks, and context-sensitive
analysis. Such frameworks (Celik 2009 , 2010 ; Ghosh et al. 2013 ; Volpi et al. 2012 ;
Bruzzone and Bovolo 2013 ) can cope more efficiently with the complexity pictured
in very high-resolution data.
Promising experimentalresults after the application of an unsupervised change
detection procedure, which is based on the iterative reweighting multivariate
alteration detection (IR-MAD) algorithm (Nielsen 2007 ; Canty and Nielsen 2008 ),
are presented in Figs. 10.2 , 10.3 ,and 10.4 . Based on the invariant properties of
the standard MAD transform where we assume that the orthogonal differences
contain the maximum information in all spectral bands, an iterative reweighting
procedure involving no-change probabilities can account for the efficient detection
of changes. In the upper row of Fig. 10.2 , the QuickBird image acquired in
2007 is shown, while the corresponding QuickBird image acquired in 2009 is
presented in the middle row. The detected changes after the application of the
IR-MAD and post-processing morphological algorithms are shown in the bottom.
All changes represent the new buildings that were constructed in the region after
2007. The detected changes/buildings are overlaid in the 2009 image and shown
with a red color. The ground truth data are shown with the same manner in
green.
In Fig. 10.3 , the IR-MAD output and the corresponding binary image after a
thresholding are shown in the upper row. The detected changes (new buildings) after
the application of a morphological post-processing procedure and the corresponding
ground truth data are shown in the bottom. All the changes (all new buildings)
have been successfully detected by the unsupervised procedure. The quantitative
evaluation reported a low detection completeness of around 60 % and a high
detection correctness of 95 %. This can be, also, observed in Fig. 10.4 where the