Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data - Computational Approaches for Urban Environments - page 239

10.5 Unsupervised Change Detection Methods

Unsupervised approaches are based on automated computational frameworks that
usually produce binary maps indicating whether a change has occurred or not.
Therefore, standard unsupervised change detection techniques are not usually based
on a detailed analysis of the concept of change but rather compare two or more im-
ages by assuming that their radiometric properties are similar, excluding real change
detection phenomenon (Bruzzone and Bovolo 2013 ). However, this assumption in
realist scenarios is not satisfied, especially, in local scales. In particular, the captured
complexity of terrain objects, with different spectral behaviors at different dates
and environmental conditions, is significant especially in very high-resolution data.
That is the main reason why although unsupervised change detection methods have
validated so far, their effectiveness on medium- to high-resolution data and usually
under pixel-based image analysis, when the spatial resolution reaches submeter
accuracies, they become less accurate (Hussain et al. 2013 ).
Unsupervised approaches have accumulated a significant amount of research
efforts since i) on the one hand, they are more attractive from an operational point
of view, allowing automation without the need for manual collection of reference
data/samples and ii) on the other hand, they can possible address the aforementioned
challenges and move towards a semantic change labeling by identifying the exact
land-cover transition.
In Table 10.4 , a summary of the recent unsupervised change detection studies is
presented. Recent methods are classified according to the core technique on which

Table 10.4 Summary of recent change detection studies classified according to their unsupervised
or supervised nature and the main technique that they were based on
Methods

Employed techniques Unsupervised Supervised

Direct comparison,
transformations, similarity
(ratios, kernels, change
vector analysis, etc. )

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 ), and Volpi et al. ( 2012 )

Brunner et al. ( 2010 ), Deng
et al. ( 2008 ), and Falco et al.
( 2013 )

Multiscale analysis
(wavelets, etc. )

Bovolo et al. ( 2013 ), Celik
and Ma ( 2010 ), Celik and Ma
( 2011 ), Dalla Mura et al.
( 2008 ), and Moser et al.
( 2011 )

Bovolo et al. ( 2009 )

Fuzzy theory Ling et al. ( 2011 ), Luo and Li

( 2011 ), and Robin et al.
( 2010 )

(continued)