Multisource data fusion results of fused optical and SAR data for Irigated rice areas identification

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Multisource data fusion results of fused optical and SAR data for Irigated rice areas identification

At the fusion step, we applied several fusion techniques and compared the fused results. Those techniques are overlay, color transformation (RGB-> IHS) and substitution, principal component analysis (PCA) and substitution and thematic combinations.

Overlay
Overlay of multitemporal data and display in different color channel of RGB. This technique is suitable to apply to single frequency or single polarization data like SAR. Not only this technique renders some colors to the interpreter but it also present changes during the acquisition period of the multidate data.
Color Transformation and Substitution
Transforming multispectral image from RGB to HIS color space, then substitute intensity with other higher resolution data like SPOT-PAN or SAR data, finally, convert back to RGB, which is the standard approach to improve the resolution of low resolution multispectral image was used. The color characteristics of the original multispetral image are maintained (Hue and Saturation are not changed) while we can observe the image at higher detail as a result of replacing intensity with high resolution image. Result shown in figure 4 is the fusion of Landsat TM with SAR.
Principal Component Analysis and Substitution
The purpose of applying principal component analysis (PCA) is to reduce the dimensionality of input data into a smaller number of output channels. It is more suitable to multispectral data where there are more bands to combine. Also, PCA could provide lesser dimensionality due to high correlating among spectral bands. This fact is well documented for all of the optical sensors presently available. In PCA, the most information of input will be transformed into the first component and the information content decreases with increasing of the number of PCA component.
Thematic Combinations
Information was derived from optical and SAR data sources separately, and they were integrated to get the fusion image. Combination of NDVI image, PCA1 of JERS-OPS image and average of multitemporal SAR data were investigated for the purpose of irrigated rice area identification.

Results and Discussions

Figure 3: Data Fusion: Overlay

Figure 4: Data Fusion : Color Transformation and Substitution (Right): Thematic Combinations

The result in figure 3 shown multitemporal SAR data of the Semarang area. White and black areas can be interpreted as no change areas during the whole period (Sep96-March97). Other primary colors, blue, green and red indicate that there are changes occurred in such areas in September, November and March respectively. Color transformation technique by replacing SAR data to intensity channel can improve interpretation accuracy of Landsat TM (30 m.) to 18 m. of JERS-SAR by retaining spectral properties of TM data. The right image in figure 4 shown the result of thematic combination. Red channel represent NDVI derived from JERS-OPS, Green represent the average backscatter of multidate SAR and blue represent first PCA of JERS-OPS. There are more colors generated from this fusion implying more information are obtained. Magenta color indicates vegetation area which is paddy area in August. Blue indicate vegetation area, green indicate non-vegetated area with some structures because it gives high backscatter while low NDVI and yellow area represent vegetation with high structure which mean forests.

Conclusion
Combining of data from different sources using thematic combination seems to be the most appropriate technique because more information can be derived. However, the information obtained from this study still requires field information to verify the applicability of fusion technique presented here. Further, fusion among multisensor and multisystems could require more parameters for instance satellite geometry, spectral band width etc. complexing the interpretation.

Reference
Soldberg, A. H., 1994, Multisource classification of Remotely Sensed Data: Fusion of Landsat TM and SAR Images, IEEE Transactions on Geosceince and Remote Sensing Vol.32 No.4 July 1994 pp768-776.
Wald L., 1999, Some Terms of Reference in Data Fusion, IEEE Transactions on Geosceince and Remote Sensing Vol.37 No.3 May 1999 pp1190.

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