ol. XXXVIII, Part 7B 
In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
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2.1 Significant Features 
The extraction of significant features performed by SURF 
based on sum of Haar wavelet responses in two directions 
(Brown, and Lowe, 2002). The Harris comer detection is the 
most widely used detector up to day due to its excellent results 
(Lindeberg, 2004). Fast-Hessian Detector is based on integral 
image and approximation. Integral image represents the sum of 
all pixels in the input image within a rectangular region formed 
by origin. Approximated Hessian detector uses box filter. 
While, scale variations are detected by different sizes of box 
filter. The next stage is to make descriptor of local gray level 
geometry feature. The local feature representing vector is made 
by combination of Haar wavelet response. The values of 
dominant directions are defined in relation to the selected 
region orientation. 
The final stage of features extraction based on Canny edge 
detector (Canny, 1986), which is popular edge operator that 
widely used in digital image processing including remote 
sensing, and lines are extracted using Hough Transform (Duda, 
and Hart, 1975). Since SURF image of magnitude and direction 
is the base layer for feature detection, we propose to adjust 
several stages of Canny operator. In our work the four stages of 
Canny operator modified into three stages. Firstly, the image is 
smoothed by Gaussian convolution. We could skip the first 
derivative operator as it provides in the SURF image. Secondly, 
the process of non-maximal suppression (NMS) is imposed on 
the smoothed SURF image. Finally, the edge tracking process 
exhibits hysteresis controlled by two predefined thresholds. 
Traditional Canny operator carries on the edge tracking 
controlled by two thresholds, namely a high threshold and a low 
threshold. The tracking of one edge begin at a pixel whose 
gradient is larger than the high threshold, and tracking 
continues in both directions out from that pixel until no more 
pixel whose gradient is larger than the low threshold. The 
process is called hysteresis. It is usually difficult to set the two 
thresholds properly, especially for remote sensing image. The 
illumination and contrast of different portions of remote sensing 
image are often non-uniform. 
The suggested process extracts long edges related to roads 
features with Hough Transform prior Canny operator. Thus roof 
detection could be implemented without predefined thresholds. 
As now the long edges related to roofs features and the edge 
tracking is carried out by inside edges. AS, it is difficult to 
detect continuous and stable edges solely from the images the 
morphological closing operation is employed. It's produced by 
the combination of dilation and erosion operations. During the 
process, the edges detected areas are integrated into the 
individual roof features. Finally, all the extracted features (roads 
and roofs) were converted from raster to vector format and 
saved as GIS project. While roofs converted to polygons, roads 
have been converted to polylines that cross-along the central 
line of detected (long edged) features. 
2.2 Topological Method 
Topological matching is usually used to reduce the search 
range or check the results of geometric matching, since it is 
seldom used alone. Topological methods can spread the 
matching into the whole network, but this requires high 
topological similarity of two data sets. Topological transfer 
method (Tomaselli, 1994) is representative of this type. If the 
polygons are matched, then according to the relationship of 
polygons and polylines, polylines to polylines matching can be 
deduced. 
Data preprocessing stage standardizes the input data sets, 
ensures the conflation data sets have a same data format, the 
same north direction, and have overlapped spatial coverage. It 
also ensures the data sets have maximum similarity which is the 
basis of common objects matching. 
A Reeb graph is a topological and skeletal structure for an 
object of arbitrary dimensions (Berg and Kreveld, 1997). In 
Topology Matching, the Reeb graph is used as a search key that 
represents shapes of the features. A node of the Reeb graph 
represents a connected component in a particular region, and 
adjacent nodes are linked by an edge if the corresponding 
connected components of the object contact each other. The 
Reeb graph is constructed by re-partitioning each region. The 
Multiresolutional Reeb Graph (MRG) begins with the 
construction of a Reeb graph having the finest resolution 
desired. Second, position of an inserted vertex is calculated by 
interpolating the positions of the relevant two vertices in the 
same proportion. Thirdly, the T-sets (connected components of 
triangles) are calculated. Fourthly, if two T-sets between 
adjacent ranges are connected, corresponding R-nodes are 
connected by an R-edge. The complete notification as follows: 
1. R-node: A node in an MRG, 2. R-edge: An edge connecting 
R-nodes in an MRG, 3. T-set: A connected component in a 
region, 4. pn-range: A range of the function pn concerning an 
R-node or a T-set. 
2.3 Weight-based Topological Map-matching 
This subsection gives an overview of how similarity is 
calculated using MRGs. A weighting approach in selecting the 
correct feature from the candidates improves the accuracy of 
correct pair identification (Greenfeld, 2002). The suggested 
algorithm assigns weights for all candidates using similarity in 
linear network and transfer matching and selects the pair with 
highest weight score as the potentially corrected CPs. The 
mathematic representation of Root Mean Square (RMS) error 
value of map-matching process estimates accuracy of algorithm 
by predefined threshold. If the algorithm fails to identify the 
correct CPs pair among the candidate pairs and RMS error 
oversize threshold then the algorithm regenerates another pair 
with lower weight by an optional loop stage. 
2.4 Test Point Error (TPE) 
The cubic B-Spline convolution supports image 
transformation and resampling, as it is computed raw-by-raw 
and column-by-column (Unser et al., 1993). The results of 
proposed convolution tested with Test Point Error (TPE). The 
test points are CPs that were deliberately excluded from the 
calculation of the mapping parameters. The concept of this 
method can be extended such that the distance between 
corresponding ‘test’ lines or surfaces is measured (Nelson et al., 
1997). TPE cannot be set to zero by overfitting. This method 
can be used only if a sufficient number of the CP’s is available. 
Otherwise, the exclusion of several CP’s may result in 
inaccurate estimation of mapping parameters. In our algorithm 
10% of all CPs are excluded for TPE evaluation. Once again, if 
the algorithm fails to transform and resample sensed image and 
TPE error oversize threshold then the algorithm regenerates 
another pair with lower weight in stage 2 (Weight-based 
Topological Map-matching) by an optional loop stage. 
3. RESULTS 
The following section presents both simulated and real-world 
results. First, we evaluate the effect of multi-temporal 
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