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Poster Session 1
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Integration of Multisensor Multitemporal Satellite Data
for Agricultural Vegetation Mapping
3.3 Parameter Selection
How to decide the thresholds x1 and
x2 is very important. It is essential that the specification of
x1 and
x2 should make the classes to be more easily separated. For different classes to be classified, VDI exhibit different change directions and magnitudes. We firstly derived the VDI values of different classes from the training data set of multitemporal satellite data, then we calculated CI values between all the same classes and different classes, finally, we deliberated the results and selected the most appropriate thresholds to try to separate all CI values into three groups evenly: increasing, decreasing and constant. We can easily decide the thresholds and divide these values into three groups. For Landsat TM data, we decided the thresholds as
x1=13 and
x2=-1, and for ERS SAR data, we decided the thresholds as
x1=12 and
x2=-2.
a and
b are user-specified parameters which control the degree of consistency between the temporal data, they were determined experimentally with respect to the overall classification accuracy.
Through an inspection of these results, the optimal choice
( aTM=0.6 and
bTM=0.0) was made that the classification performance was best, and the optimal choice of
aSAR and
bSAR respect to classification accuracy is
aSAR=0.7 and
bSAR =0.0. For the determination of reliability factors
lT and
lS, in our MTFC model,
lT and
lS do not simply represent the reliability of different data sources, but the reliability of the proposed TFC model applied on multiple source data. Thus, the determination of
lT and
lS rely on classification results of the TFC model for multitemporal Landsat TM and ERS SAR data.
4. Results and Discussion
The performances of the proposed TFC technique are assessed and compared with the conventional Maximum Likelihood Classification (MLC) method. To provide a reference for comparison, the single period images are first analyzed separately, the performances of the MLC method are 73.3% and 73.1% correct for the May 1994 and August 1994 Landsat TM data, respectively, and 24.5% and 22.1% correct for the July 1994 and September 1994 ERS SAR data, respectively. The results of multitemporal classification are substantially better than either of single period performances. The results demonstrated by the proposed TFC method show an overall accuracy of 89.0% and 39.6% for Landsat TM and ERS SAR data, respectively, these are truly better than the MLC method of 85.5% correct performance for TM data and 39.1% correct performance for SAR data. Moreover, 10 of the all 12 classes show improved accuracy using the proposed TFC model compared to the MLC method for Landsat TM data. The results indicate that the proposed TFC method can be applicable for multiple source satellite data which have different characteristics. Furthermore, the computation time using the TFC method is less than half of the time used by the MLC method.
For multiple source classification of multitemporal Landsat TM and ERS SAR images, the proposed multisensor temporal fusion classification (MTFC) model shows improved 89.1% correct performance, compared to an overall accuracy of 88.9% derived by the simple combination temporal fusion classification (SCTFC) model which ignores the reliabilities of different data sources, and much better than the MLC method of 86.4% correct performance.
5. Conclusion
We proposed a new method based on the Bayesian formulation and try to integrate multisensor multitemporal satellite data for accurate mapping of agricultural area. This is a statistical method based on multisensor multitemporal data fusion, which takes into account the temporal dependence of images and the reliabilities of different data sources. In the proposed TFC model, the class-dependent likelihood of multitemporal data are calculated, respectively, and the transition probabilities are estimated from the change pattern of VDI between the same and different classes of images, then the Bayes optimal classification is performed by maximizing the set of the class-dependent likelihood and the transition probabilities. The proposed TFC model was further extended and can be applied for classification of optical and SAR satellite images. This method can also be used as an alternative method for change detection of land cover and land use. Experimental results showed that the fusion model is robust and can improve the classification accuracy, moreover, reduce the dimensionality of the probability functions used and facilitate the computation over time.
References:
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- Oguma, H., and Yamagata, Y., 1997, Study on effective observing season selection to produce the wetland vegetation map. Journal of the Japan Society of Photogrammetry and Remote Sensing, 36, 5-16.
- Schistad Solberg, A. H., Jain, A. K., and Taxt, T., 1994, MultiSource classification of remotely sensed data: fusion of Landsat TM and SAR images. I.E.E.E. Transactions on Geoscience and Remote sensing, 32, 768-777.
- Swain, P. H., 1978, Bayesian classification in a time-varying environment. I.E.E.E. Transactions on Systems, Man, Cybernetics, SMC-8, 879-883.
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