Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data - Computational Approaches for Urban Environments - page 245

joint object-based data mining framework during which instead of the standard
supervised classification step, a data mining algorithm was employed to generate
decision trees from certain training sets. Schneider ( 2012 ) proposed an approach
that exploits multi-seasonal information in dense time stacks of Landsat imagery
comparing the performance of maximum likelihood, boosted decision trees, and
support vector machines. Experimental results indicated only minor differences in
the overall detection accuracy between boosted decision trees and support vector
machines, while for band combinations across the entire dataset, both classifiers
achieved similar accuracy and success rates.
This observation is in accordance with similar recent studies (Table 10.4 )which
employ powerful machine learning classifiers (Bovolo et al. 2010 ; Chini et al.
2008 ; Camps-Valls et al. 2008 ; Habib et al. 2009 ; Pacifici and Del Frate 2010 ;
Demir et al. 2012 ; Pagot and Pesaresi 2008 ; Taneja et al. 2013 ; Volpi et al. 2013 )
for supervised change detection and indicate why they are so popular for remote
sensing classification and change detection problems. However, machine learning
algorithms are, usually, time-consuming and efforts towards a more computational
efficient design and algorithmic optimization are required (Habib et al. 2009 ).
Moreover, in local scales and very high-resolution data, including 3D or vector
data, there is a lot of room for research and development in order to exploit the
entire multimodal datasets. In particular, an important outcome from the recent 2012
multimodal remote sensing data contest (Berger et al. 2013 ) indicates that none
of the submitted algorithms actually exploited in full synergy the entire available
dataset, which included very high-resolution multispectral images (with a 50 cm
spatial resolution for the panchromatic channel), very high-resolution radar data
(TerraSAR-X), and LiDAR 3D data from the city of San Francisco, USA.
Therefore, in local scales, but not only, novel sophisticated, generic solutions
should exploit the recent advances in 2D and 3D building extraction, reconstruction,
and 3D city modeling which have gain a lot of attention during the last decade
due to emerging new engineering applications including augmented reality, virtual
tourism, location-based services, navigation, wireless telecommunications, disaster
management, etc. In a similar manner like the post-classification change detection,
monitoring the structured environment, both in 2D and 3D, can be based on the
recent advancements on building extraction and reconstruction by, for instance, a
similar direct comparison between two different dates. In the following subsection,
recent building detection and modeling methods are briefly reviewed.

10.7 Computational Methods for 2D and 3D Building

Extraction and Modeling

The accurate extraction and recognition of man-made objects from remote sensing
data has been an important topic in remote sensing, photogrammetry, and computer
vision for more than two decades. Urban object extraction is, still, an active research
field, with the focus shifting to object detailed representation, the use of data from
multiple sensors, and the design of novel generic algorithms.