Object Based Image Analysis (OBIA) gained recently a lot of
attention among the geographical mapping applications as an
alternative analysis framework that can avoid the drawbacks
associated with pixel based analysis. In spite of the advantages
of pixel based image analysis, it suffers from problems such as
sensitivity to variations within objects significantly larger than
pixel size (Alpin et al., 2008). The spatial extent of the objects
to be classified is of more importance to the classification task
than the spatial scale of image pixels (Platt et al., 2008). Object
based classification can remarkably improve the classification
accuracy by relieving the problem of misclassifying individual
pixels (Alpin et al., 1999).
The proposed approach, presented in this paper, uses a single
return LIDAR data along with aerial images to extract
buildings, and trees of urban areas. Object based analysis is
adopted to segment the entire DSM data into objects based on
height variation. The classification task is based on two stages
where the primary classified objects can help to derive new
feature which is the height to ground for the second stage.
Among the many features provided by the aerial imagery, a
normalized difference vegetation index based on R and IR
bands have been used due to its high significance in vegetation
extraction. The second classification stage uses the object size,
average height to ground, and the vegetation index to fine tune
the classification of objects.
The following section demonstrates the steps of the proposed
approach. Then, experimental results of the proposed approach
for different urban areas are presented. Finally, the conclusions
are provided.
2. METHODOLOGY
The proposed approach, adopts object based analysis where
objects are the targets for classification. The first step is to
perform image segmentation on DSM height image to divide
the whole scene into objects. A region growing algorithm is
conducted over the entire DSM height image starting from the
upper left corner based on neighbourhood height similarity. The
same traverse of data during object’s extraction is exploited to
calculate the area of each object to be used in the classification
step.
Based on the neighbourhood height similarity used in the
segmentation step, the points of each extracted object tend to
belong to the same object plane. Planar objects such as ground
and building surfaces will exhibit large patches as they maintain
smooth height changes. On the other hand, trees typically
exhibit high variation of height due to the frequent LIDAR
penetrations of its crowns. Consequently, trees areas exhibit
small areas.
As a preliminary classification, objects under minimum area
threshold are classified as vegetation; this threshold represents
the smallest expected area of a building object and was selected
as 10 m? during our tests. The rest of objects are classified as
buildings except for the largest object which is classified as
ground. The largest object is used as a height reference, and the
height to ground of each pixel of the rest of area is calculated as
the difference between the pixel height and the nearest ground
pixel height.
Due to the interpolation applied to the LIDAR data, some walls
of the buildings exhibit misleading high height variation that
results in small patches misclassified as vegetation, the same
misclassification is encountered for the architectural details of
buildings as they also show abrupt height changes over small
areas. These misclassifications are revised during the second
classification stage.
For finding the corresponding spectral data of the extracted
objects, an ortho-photo of the scene is constructed using all the
overlapping images over the scene. All the ortho-rectified
images that intersect with the scene boundary are merged
together to obtain a true ortho-photo of the scene where the
occluded or invisible areas in an ortho-photo is complemented
by the other ortho-photos from the other images. Figure |
illustrates sample ortho-photos of an area along with the merged
true ortho-photo obtained.
1.a an ortho-photo with partial — 1.b an ortho-photo with partial
Scene coverage coverage for the same scene
l.c an ortho-photo with partial 1.d the overall true ortho-
coverage for the same scene photo of the scene
Figure 1. True ortho-photo generation
Normalized Difference Vegetation Index (NDVI) is computed
for all objects in the scene using IR and R bands of the
generated true ortho-photo as in (1)
(R-R)
(IR+R)
NDVI = (1)
The second stage of classification is conducted to tune the
preliminary classification of the first stage according to the
following rules:
e Objects of high height-to-ground (70.2) and high
NDVI (>0.18) are classified as trees.
e Objects of high height-to-ground (70.2) and low
NDVI («0.18) are classified as buildings.
e Objects that do not satisfy the previous two conditions
maintain their preliminary classification.
3. RESULTS
To evaluate the proposed approach, both aerial images and
LIDAR data of three urban areas in the centre of the city of
Vaihingen are used for testing. These data sets are provided by
ISPRS test project on urban classification and 3D building
reconstruction. These areas have historic buildings with rather
complex shapes, few high-rising residential buildings that are
surrounded by trees, and a purely residential area with small
detached houses. The digital aerial images are a part of the high-
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