2.3 Multisensor Data Fusion Modelling
For integration of multisensor multitemporal satellite data, let us consider the m-variate observation feature vectors of Landsat TM data
X
Tt and the set of possible land cover classes
C
it (i =1, 2, …, n) related to time
t (t =1, 2, …, p), also the d-variate observation feature vectors of ERS SAR data
X
Sg and the set of possible land cover classes
E
ig(i=1, 2, …, n) related to time
g(g=1, 2, ..., q). For each data source, it is suggested to allow different data sources to be weighted differently according to some measure of their reliability (Benediktsson and Swain 1992). Thus, the likelihood function that both the temporal attribute of single-source data and the reliability of multi-source data are concerned can be derived to take the following form:
Here,
lT and
lS,
0<
lT,
lS<1 represent the reliability factors associated with the temporal data fusion performance on multitemporal Landsat TM data and ERS SAR data, respectively.
3. Experiment
3.1 Study Area
The study area is located in the region of Zhangwu County, a typical agricultural area of Liaoning province in the northeast part of China.
The image size is 1024 x 1024 pixels, corresponding to 25.6 x 25.6 km
2, with geographical coordinates from
42
°22
¢6
²N to
42
°35
¢50
²N in latitude and from
122
°12
¢56
²E to
122
°31
¢50
²E in longitude.
Zhangwu's economy is oriented toward agriculture, producing mainly crops such as corn and grain, as well as soybean, wheat, pachyrhizus, peanut, and tobacco. The main crop season in this area is from April to October, including the driest month July. The ground survey trip was carried out in July 1999. The classification of the following land cover classes is considered: corn, paddy, soybean, wheat, meadow, poplar, pine, bare land, urban, water, dried area and deforest area.
3.2 Data Acquisition and Preprocessing
In this study, two Landsat TM images and two ERS-1 SAR images were applied for investigating the performance of multisensor temporal fusion model. The multitemporal Landsat TM images collected on 19 May 1994 and 23 August 1994 were selected mainly because these are the best available cloud-free scenes in the crop season. Relative radiometric correction was performed on multitemporal Landsat TM images so that both the influences of atmospheric conditions and sun elevation angle were removed (Oguma and Yamagata 1997). The two ERS-1 SAR images were acquired on 14 July 1994 and 9 September 1994. For the preprocessing of SAR images, the 16-bit data were firstly converted to 8-bit data. Then, two passes of filtering were performed in order to reduce inherent noise, the first filtering was done by using 3 by 3 median filter, the second iteration using 5 by 5 mean filter. The multitemporal TM and SAR images were co-registered and resampled to 25m pixel size, then geometrically corrected in UTM projection using the nearest neighbor method. The RMS error less than 1 pixel was yielded. Only six channels of TM data were used except the thermal channel 6. NDVI data was extracted and scaled to integer value from 0 to 200 to be used as VDI. For temporal data fusion of ERS SAR data, we used the digital value of SAR backscattering coefficient directly as VDI.
