The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3h. Beijing 2008
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3.3 FCM Clustering in Spectral Domain
In this step of proposed algorithm, FCM clustering technique is
implemented by defining number of cluster centers (C) and
fuzzy parameter m in spectral space, in this work, cluster
centers were not selected randomly. For definition of cluster
centers, at first C-Means clustering technique was done by
defining C and the output of this technique were entered the
FCM as input cluster centers. The number of cluster centers (C)
is defined according to diversity of image objects and by try
and error operation.
After performing the clustering technique, by supervision of
human expert and definition of suitable threshold, road class
binary image is obtained.
3.4 Post-processing of Images
After selection of road class binary image, since the obtained
class is not clean and misclassified pixels are in the image, post
processing operation is mandatory in order to increase
classification accuracy.
In this research morphological operators are used to improve
the image by removing noise and filling gaps. These operators
consist of Opening, Closing, Bridge, Clean, Majority and
Thicken which are used according to structure of misclassified
pixels in the road class binary image.
3.5 C-Means Clustering in Spatial Domain
According to section 3.3, the FCM clustering was done on the
grey level values of the pixels in spectral bands of the image. In
this step, C-Means clustering is performed on the spatial
coordinates of the pixels. So initially, a uniform grid of points is
overlaid on the road class binary image and by using C-Means
clustering; the location of some predefined nodes is changed.
These points are called "Active Nodes". Nodes that their
locations remain unchanged are called "Dead Nodes". Dead
nodes are simply disregarded for the remainder of the
process.
Grid spacing is based on a pre-specified (e.g., operator
provided) and differs in different images based on the shape
and width of roads in the image.
3.6 Using MST to Extract Road Centerline
In order to extract road centerline, Active Nodes should be
connected using MST algorithm. In this application Kruskal
algorithm is used to connect Active Nodes and defining road
shape. Euclidean distances of all Active Nodes from each other
are computed and introduce to the Kruskal algorithm as the
weights. Based on what was mentioned in section 2.3, the
minimum spanning tree which connects all the Road centerline
nodes, are obtained.
This product can be imported into GIS environment in vector
format.
3.7 Accuracy Assessment
Accuracy assessment of the proposed road extraction system is
performed in two steps: Classification accuracy assessment and
Road centreline extraction accuracy assessment.
Classification Accuracy Assessment
In this step, Confusion matrix is made using Ground truth data
and classified data and accuracy assessment parameters such as
Overall Accuracy (OA), Kappa coefficient, User Accuracy
(UA), Producer Accuracy (PA), Commission & Omission Error
O C
( e , e ) are calculated.
These parameters are calculated before and after post
processing of images by means of morphological operators.
Road centreline extraction accuracy assessment
In order to obtaining the differences between manual and
automatic extracted road centerlines, distances between these
lines are measured in location of Active Nodes. Then road
centerline RMS Error is calculated using these measurements in
x and y directions.
4. EXPERIMENT WITH IMAGERY
After getting familiar with the proposed semi-automatic road
extraction system procedure, the time is ripe to explore the
practical results of implementing the system on test images.
Fig. 3 denotes the procedures of semi-automatic road centreline
extraction from pan-sharpened IKONOS image of Lavasan city
in Iran (lm resolution and 119x170 pixels) in mountainous
area. Fig. 3b shows the FCM clustering of input image and
resulting road class by defining threshold. It is clear that
the details of the clustering are immaterial for the
purposes of this investigation but required parameters are
presented in the table 1. Fig. 3c shows the result of using
morphological operators as post processing operation.
What is important is the existence of classification
accuracy assessment parameters before and after post
processing which are accessible in table2.