Integration of aerial images and lidar data
compensate for the individual weaknesses
of each data set when used alone, thus
providing more accurate classification of
terrain cover, such as buildings, roads and
green areas, and advancing the potential for
automation of large scale digital mapping
and GIS database compilation. This
paper presents work on the development
of automatic feature extraction from
multispectral aerial images and lidar data.
A total of 22 feature attributes have been
generated from the aerial image and
the lidar data which contribute to the
detection of the features. The attributes
include those derived from the Grey Level
Co-occurrence Matrix (GLCM), Normalized
Difference Vegetation Indices (NDVI), and
standard deviation of elevations and slope.
A Self-Organizing Map (SOM) was used for
fusing the aerial image, lidar data and the
generated attributes for building detection.
The classified images were then processed
through a series of image processing
techniques to separate the detected
buildings. Results show that the proposed
method can extract buildings accurately.
Compared with a building reference map,
95.5 percent of the buildings were detected
with a completeness and correctness of
83 percent and 80 percent respectively
for buildings around 100m2 in area; these
measures increased to 96 percent and 99
percent respectively for buildings around
1100m2 in area. Further, the contributions
of lidar and the individual attributes to the
quality of the classification results were
evaluated. |
