Geoscience Reference
In-Depth Information
2012
) or a single SAR one (Ferro et al.
2013
). Image-based 3D reconstruction has
been, also, demonstrated from user-contributed photos (Irschara et al.
2012
)and
multiangular optical images (Turlapaty et al.
2012
).
Experimental results demonstrating the performance of supervised classification
algorithms combined with post-classification procedures for building extraction
from high-resolution satellite data are shown in Figs.
10.5
and
10.6
.
Standard pixel-based classification algorithms like the minimum distance, maxi-
mum likelihood, and SVMs deliver detection outcomes with a low correctness rate.
In particular, in the upper left part of Fig.
10.5
, the raw Pleiades image acquired
in 2013 is shown. The result from the minimum distance algorithm, showing
only classes related to buildings, is shown in the upper right part of the figure.
The quantitative evaluation reported a low detection overall quality of 62 % for
the minimum distance algorithm. With the same ground samples, the maximum
likelihood algorithm reported an overall detection rate of 67 % and the result is
shown in the middle row (left). The SVM classifier scores higher with an overall
detection quality of 74 % (middle right).
After post-classification procedures, including mathematical morphology, object
radiometric and geometric properties calculation, and spatial relation analysis, the
result from the supervised classification has been refined and its correctness rate is
significantly improved. The detected buildings, based on the SVM output, which
have been recognized and labeled by the algorithm, are shown in 2D with different
colors in the bottom row of Fig.
10.5
(left). The detected buildings overlaid on the
raw Pleiades image are, also, presented in the bottom right of Fig.
10.5
.Moreover,
the low detection rate can be observed in Fig.
10.6
where the detected buildings
are presented. In particular, the detected buildings are shown in 3D, in the top of
Fig.
10.6
, while all scene buildings are shown in the bottom as they have been
extracted from the ground truth data.
10.8
Conclusion and Future Directions
Computational change detection is a mature field that has been extensively studied
from the geography, geoscience, and computer vision scientific communities during
the past decades. An important amount of research and development has been
devoted to comprehensive problem formulation, generic and standardize proce-
dures, various applications, and validation for real and critical earth observation
challenges.
In this review, we have made an effort to provide a comprehensive survey of the
recent developments in the field of 2D and 3D change detection approaches in urban
environments. Our approach was structured around the key change detection compo-
nents,
i.e., (i)
the properties of the change detection targets and end products;
(ii)
the
characteristics of the remote sensing data;
(iii)
the initial radiometric, atmospheric,
and geometric corrections;
(iv)
the unsupervised methodologies;
(v)
the supervised
methodologies; and
(vi)
the building extraction and reconstruction algorithms.
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