From expressions (2) and (3), we can obtain:
log, O(D | E, ) = log, O(D)+ Ww,
log, O(D | E,) - log, O(D)- W; (4)
Similarly, if more than two evidence maps are used, they can be
added by assuming that all the evidence maps are also
conditionally independent with respect to the occurrence of a
class. Therefore, we can obtain,
log, O(D | E! E: E!) - log, O(D)- Y W/
e ne
where the superscript & refers to the presence or absence of the
evidence, respectively.
As mentioned previously, the WOE assumes that all the
evidence patterns are conditionally independent. However, in
most applications, = evidence patterns are conditionally
dependent. Thus, posterior probability estimates by directly
using WOE are likely to be biased upwards when this
assumption is violated. In this study, we adopted a modified
WOE model, which is called the conditional dependence
adjusted weights of evidence (CDAWE) (Deng, 2009), to
correct the bias using the correlation structure of the evidence
patterns.
2.2 Fusion of multi-sensor data for land cover classification
In this study, the Support Vector Machine (SVM) was first
applied on different data to produce land cover classification
results. The SVM classifier is a recently developed statistical
learning method, which has been widely used in remote sensing
image classification and showed very good performance (e.g.,
Gualtieri and Cromp, 1998; Huang et al., 2002; Melgani and
Bruzzone, 2004). The obtained urban classification results were
then fused by weights of evidence model. The estimation of
conditional probability for each class is crucially important,
since it directly related to the weight of each evidential map. In
this study, the intermediate classification results derived from the
SVM classification, i.e. posterior probability of classification,
either from classification rule image as in ENVI or using the
method proposed by Platt (2000) were used as the initial
conditional probability of each class. Since classification results
from different data show different accuracies (i.e. class
reliability), final conditional probability for each class is
obtained by combining conditional probability from the initial
classification and class reliability (i.e. class accuracy). Since the
conventional weights of evidence method is defined for one-
class extraction (e.g. mineral occurrence), in order to extend it to
multi-class classification, the weights of evidence method was
first used in classification to produce a posterior probability for
each class, and then the class for each pixel is estimated by
taking the most probable class of the posterior distribution (i.e.
with highest posterior probability).
3. EXPERIMENTAL RESULTS AND DISCUSSION
The proposed method was evaluated and validated in land cover
classification using two examples. The first example includes
Landsat TM and multitemporal ENVISAT ASAR data,
covering Beijing urban-suburban area, China. The second
example includes Landsat ETM+ and SPOT 5 multispectral
images of Hengshui area of Hebei Province, China. Landsat
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
TM/ETM+ and ENVISAT ASAR data have a 30m spatial
resolution, whereas the SPOT 5 data have a 10 m spatial
resolution (band 4 has 20 m spatial resolution).
Results showed that the combination of multi-sensor data using
weights of evidence model produced higher classification
accuracy than the use of single data alone. For example, for
Beijing area, combined classification based on WOE produced
41.72% and 3.22% of increase in Kappa coefficient, compared
with those from SAR and TM images, respectively (Table 1).
For Hengshui area, combined classification based on WOE
produced 7.08% and 6.22% of increase in Kappa coefficient,
compared with those from ETM+ and SPOT 5 images,
respectively (Table 2). It seems that when two individual
classification results show comparable accuracy, the increase in
classification accuracy (both overall accuracy and Kappa
coefficient) produced by WOE based decision fusion is more
significant (i.e. in Table 2).
Figures 1 and 2 show portions of classifications using
difference data combinations for two examples. From these
figures, WOE effectively fused the classification results from
different results. For example, salt-pepper appearance was
significantly reduced.
4. CONCLUSION
A decision fusion method based on the weights of evidence
model was proposed in this study and evaluated in land cover
classification using two different es. Results showed that the
combination of different data using weights of evidence model
in land cover classification produced significantly higher
accuracy (both overall accuracy and Kappa) than the use of
single source of data alone.
In conclusion, the weights of evidence model provides an
effective decision fusion method for improved land cover
classification using multi-sensor data.
5. REFERENCES
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Deng, M., 2009. A Conditional Dependence Adjusted Weights
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