Buildings are one of the most important geospatial
features for spatial analysis and mapping. Building extraction
has been an active research topic in computer vision as well as
digital photogrammetry in recent years. Building detection is
the process of obtaining the approximate position and shape of
a building, while building extraction can be defined as the
problem of precisely determining the building outlines, which
is one of the critical problems in digital photogrammetry.
Building information is extremely important for many
applications such as urban planning, telecommunication,
three-dimensional city modeling, or extraction of unauthorized
buildings over agricultural lands. Three approaches for
building detection based on maximum likelihood classification
have been compared, firstly, building detection from
classification of multispectral satellite image only. The second
approach is building detection from classification of multispectral
satellite image, while the height information from
Light Detection and Ranging (LIDAR) data is applied as an
additional channel together with spectral channel. The third
approach is building detection based on classification of
multispectral satellite image where normalized difference
vegetation index (NDVI) and the height information from
LIDAR data are applied as additional channels together with
spectral channel. The contributions of the individual cues
used in the classification have been evaluated. The three
approaches were tested using urban blocks containing
different sizes, roof color and shapes of buildings. The
results show that the third approach is the best for building
detection followed by the second approach then the first
approach. The third approach appears to be quite successful
especially in solving the problem of building detection for
those urban blocks that contain closely located buildings as
well as in separation of buildings from trees. The third
approach results have been improved by developing a
building detection module based on integration of classified
image, elevation data, and spectral information. A rule-based
expert system consists of essentially hypothesis (output;
buildings), and variables of a knowledge base were developed
in the knowledge engineer of ERDAS Imagine for postclassification
refinement of initially classified output building
mask. Classification rules were enriched with ancillary data
such as the normalized digital surface model and the NDVI.
Each rule is a representation of each node in the tree that
describes a building class or probability of presence of
buildings pixel. Then, the building detection result has been
evaluated. It has been found that the use of an expert system,
which considers expert knowledge, would further help in the
discrimination of the classes and improve classification
accuracy of buildings. The overall accuracy of expert
classification was 96% and kappa coefficient was 0.95. |
