Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data - Computational Approaches for Urban Environments - page 243

Fig. 10.3 The detected under an unsupervised manner changes (buildings) and the corresponding
ground truth data. Upper row : A map with the possible changes after the application of the
regularized iteratively reweighted MAD algorithm ( left ) and after thresholding ( right ). Bottom row :
The detected changes (buildings) after the application of morphological post-processing ( left )and
the ground truth ( right ). All the changes (new buildings) have been successfully detected. The
quantitative evaluation reported a low detection completeness of around 60 % and a much higher
detection correctness of 95 %

detected changes have been associated with the corresponding DEM. The detected
new buildings in 3D are shown in the upper part of Fig. 10.4 , while the 3D buildings
from the ground truth data are shown in the bottom.

10.6 Supervised Change Detection Methods

The supervised classification approaches traditionally are based on the detection of
changes from a post-classification process (which is usually another classification).
This process enables, also, the detection of actual class transitions instead of a binary
“change or not change” product. However, errors from each step and each individual
classification are propagating and are summed up at the end product. Moreover,
collecting reliable, dense training sample sets can be difficult and time-consuming
for certain cases ( e.g., historical data) or even unrealistic if one has to deal with
extensive dense time series and multimodal data. In practice, however, the post-
classification approach is, nowadays, the most standard one especially for global
and regional scales, for land-cover, land-use, and urbanization monitoring.
In more local scales and for very high-resolution data, the standard supervised
approach is an object-oriented one under an object-based image analysis framework