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 post-classification 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 |
