3. Methodology
The raw SAR data were converted to obtain SAR backscatter (beta nought-b0) in Db using the formula derived by ALTRIX System (1998). Then this log-scaled (b) image was converted into a power-scaled image plane. The power- scaled SAR data were filtered using frost and gamma-map adaptive filters. All the filtered images were co registered with reference to July 15,1998 image. The July 15,1998 image was a transitional one in between pre flooding and flooding condition. The July 15,1998 SAR data was georeferenced using 1:50,000 scale SPOT hardcopy satellite image. The Georeferncing was perfumed using a first order transformation with cubic convolution resampling at 6.25m resolution.

The processing steps for IRS ID PAN data include co registration with SAR data and was georeferenced using a first order transformation with nearest neighborhood resampling. The purposes of processing of the SAR was to produce a multi-temporal dataset. The IRS ID PAN was processed to produce a base map and to obtain an understanding of land use/cover in the study area.

The images were filtered using 3*3,5*5 7*7 9*9 and 11*11 kernel sizes for the gamma map and frost filters. The results were checked visually and the 11*11 kernel sizes was found better than the other kernel. An unsupervised classification was performed on the both filtered datasets into 140 classes. The signature derived from this operation were analyzed and modified. These modified signature were used to classify the multi temporal dataset using the maximum likelihood classifier. Six broad classes of land use/cover depicting the pattern of dynamics of landuse and water (table 2) were produced from this operation. In the floodplain, the seasonal open water flood extent and change in crop growth stage were of interest. The landuse/cover types were considered according to the dominant crops in Kharif 11 season.

Table 2. Land use/cover classes used in this study

Class no	Class name
1	Settlements
2	Permanent water
3	Seaconal flooding
4	Rice (i.e. Aman)
5	Jute
6	Sugarcane

4. Result and Discussions
The supervised maximum likelihood classification on a Gamma-map filtered data for land use/cover type is presented in Table 3 and the same over a Frost filtered dataset is presented in Table 4. Comparison of these two filtered datasets shows differences in settlement, permanent water, seasonal water, aman and sugarcane classes were 87%, 67%,68% 27% and 90%, respectively from the Gamma-map seasonal water , aman and sugarcane classes were 76% 73% 94%75% and 53%, respectively. The overall accuracy of the entire classification from Gamma-map and Frost filtered data ser was 64% and 69% respectively.

Table 3: Classification result over Gamma-map filtered dataset

Table 4. Classification results over Frost filtered dataset
The jute class had similar for the differently filtered dataset. However, in the classification process, the jute class had a different problem Normally, Jute is planted in early March and is harvested in July- August . In classifying jute, the deficiency eal in the available time series of SAR data. In higher land, generally rice (Aman) is planted the same plots immediately after jute is harvested. But due to excessive flooding in 1998. the farmers were unable to plat rice tn these plots.

In Bangladesh, only 5% areas of the settlement area are urban settlements with concrete buildings and other structures. The remaining 95% are in rural settlements and most of the houses are constructed of straw, tin shade bamboo, wood, etc,. Due to these complex cover types, it was difficult to identify these rural settlements from the SAR data. In the analysis of the signature, some limitations were found, especially in case of smooth hard -surface roads. These feature have a signature similar to that of permanent water. Another limitation was found in the SAR imagery the presence of heavy rainfall, where backscattering was higher by around 4 dB over water areas.