shadow objects in some direction around high buildings, so we 
can distinguish these two objects by the adjacency information. 
5) Relation feature: We define relation feature as follows: if 
object A and B are two adjacent objects and A and B are the 
same class then A has relation with B. If B has relation with C 
and C is not adjacent with A then A has relation with C. 
Relation feature is the number of objects has relation with A. 
D. Rule-Based Classification 
The rule-based approach allows the analyst to combine different 
features of objects in order to assign a class membership degree 
(between 0 and 1) to each object based on a fuzzy membership 
function or strict thresholds (Benz, 2004 and Walker, 2008). 
The membership functions used in this study are based on the 
logical operator AND (&) and thresholds. Furthermore, it has a 
hierarchical capability to classify the entire scene into general 
classes (e.g., vegetation and non-vegetation areas) These 
general classes are called parent classes. Then, each parent class 
is divided to sub classes (child class) containing more detailed 
land cover types (e.g., buildings and roads). This hierarchical 
capability allows the developer to incorporate objects in 
different levels of segmentation for individual levels of class 
hierarchy. In this paper, we developed a  rule-based 
classification scheme that allows the image to be hierarchically 
classified using different spatial measures for different sets of 
classes. 
Several rules can be applied for a segment. The more rules are 
satisfied for a segment, the more likely it is that the result is 
accurate. This provides a means for increasing or decreasing 
classification certainty while combining knowledge contained 
in the rules. Also, the quality results of a rule-based system 
depend on rule weighting, since they do not have the same 
importance. The importance of each rule can change according 
to the application's context. There is no automatic method to 
determine rule weighting (Voirin, 2004). Thus, the user must 
define a weight for every rule based on his experience and 
knowledge of the environment. In this study, we propose a 
semiautomatic technique to help the user determine the rule 
weighting. The technique works as follows: The classification 
rules are applied separately, and the results are compared to the 
reference data. This yields the identification accuracy for each 
rule, which is the proposed rule weight. 
3. EXPERIMENTAL ANALYSIS 
The experimental analysis was carried out on a HS image 
acquired over the city center of Pavia (Italy) by the ROSIS-03 
(Reflective Optics Systems Imaging Spectrometer) HS sensor. 
The image has a geometrical resolution of 1.3 m and 785x300 
pixels. The original data are composed of 115 spectral bands, 
ranging from 0.43 to 0.86 um with a band of 4 nm. However, 
noisy bands were previously discarded, leading to 102 channels. 
The thematic classes found were Water, Tree, Meadow, Self- 
blocking Bricks, Soil, Asphalt, Bitumen, Tile, and Shadow. The 
training and test sets were composed of 4536 and 53539 
samples, respectively. The training set was used for training an 
SVM classifier, while the test set was employed for estimating 
classification accuracy. The true color representation of the 
image and reference data is shown in Fig. 2. 
   
    
     
Water 
a Bare soil 
[] Asphait 
(a) (b) 
Figure 2. ROSIS Pavia data sets: (a) True color representation 
and (b) Reference set 
First, marker selection was performed, using two different 
Segmentation techniques discussed in the previous section. 
Then the multiclass one-versus-one SVM classification, with 
the Gaussian radial basis function (RBF) kernel, of the HS 
image was performed. The optimal parameters C (penalty 
during the SVM optimization) and J (spread of the RBF 
kernel) were chosen by fivefold cross validation: C=128, 
ES ; ; ; ; 
y =2 ‘The results of the pixel-wise classification were 
combined with the segmentation results using the majority 
voting approach. 
Then, the MSSC-HSEG segmentation of the image was 
performed, in this experiment, the MSSC-HSEG algorithm has 
been run until no more merging was possible. Since the image 
of this urban area contains classes with mostly dissimilar 
=[0.00.20.5]. The 
proposed Rule-based classification method uses a rule base that 
combines spectral, textural, geometric, and contextual 
information. 
spectral responses, we choses 
wght 
  
  
  
  
  
  
  
  
  
  
  
  
MSSC- MSSC- Proposed rule-based 
MSF HSEG+M approach 
V 
dt 0.0 0.0 0.2 0.5 
wgnt 
OA(%) 94.43 96.90 98.88 | 98.98 | 98.11 
AA(%) 95.76 96.55 97.91 | 98.16 | 96.98 
K (%) 93.94 95.82 97.82 | 98.04 | 97.09 
  
  
Table 1.Classification Accuracies for the Center of Pavia Image: Overall Accuracy (OA), Average Accuracy (AA), Kappa 
Coefficient (x), The highest accuracies are bolded in each category. 
16 
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