wind velocity vectors, ice motion vectors, or sample 
data with text annotation may be overlayed on raster 
images. For example, sparse data such as produced 
by the ERS-1 Scatterometer or the DMSP Passive 
Microwave Special Sensor Microwave Imager (SSM/I) 
may be overlayed on ERS-1 SAR imagery or NOAA 
AVHRR imagery to produce an easily read, multisensor 
image on screen or hardcopy. 
6.3 Sparse Data Interpolation 
Sparse data requires special processing to fill in the 
points between samples when image fusion with SAR 
imagery is required. For example, sample spacing 
in an ERS-1 SAR image is normally 12.5m. The 
sample spacing of SSM/I data is 25km. Thus with 
respect to the SAR image, the SSM/I data is very 
sparse and must be interpolated. EV-SAR has several 
interpolation functions for sparse data. These are 
based on signal models for the sensor. Dispersion- 
based techniques use a resolution model for the sensor 
to interpolate the data points. Interpolation techniques 
use a spatial model of the data to interpolate points on 
a surface. 
Sparse data interpolation is done in three steps. 
First, a blank image (the slave image) is filled with zero 
pixel values and is coregistered to the SAR (master) 
image. Second, the sparse data points read from a 
tabular or vector file are placed in the blank image 
using the lat./long. georeferencing to map the data 
samples into pixel coordinates. Finally, the third step 
is the dispersion or interpolation to fill in all the pixels 
in the slave image. 
6.4 RGB and IHS Image Fusion 
Image-Image fusion may done in EV-SAR using 
standard Reg-Green-Blue (RGB) and Intensity-Hue- 
Saturation (IHS) multichannel image combination 
techniques. One application of IHS image fusion is the 
combination of ice concentration data (derived from 
the DMSP SSM/I passive microwave data) with a SAR 
image showing in detail the ice flows[8]. A total of three 
categories of ice concentrations (thin ice, first year ice, 
and old ice) are coregistered to the ERS-1 SAR im- 
age, sparse data interpolated, and converted to IHS. 
The hue and saturation channels then contain the ice 
concentration information. The intensity channel is re- 
placed by the ERS-1 SAR image, and the IHS data set 
is then converted back to RGB for display. The result 
is an ERS-1 SAR image coloured to represent green 
for thin ice, blue for first year ice, and red for old ice. 
The more saturated the colour, the greater the ice con- 
centration. Sample point data and coast lines may be 
overlayed completing the data product. An example 
of an ERS-1 SAR image with sample data overlay and 
latitude /longitude grid is given in Figure 15. 
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Figure 15: Example ERS-1 SAR image with sample 
data overlay (Copyright ESA 1992). 
7. CONCLUSIONS 
Users have found the EV-SAR application package 
to be a powerful low-cost solution for working with 
SAR imagery. With the recent availability of the 
new satellite SAR imagery (ERS-1 1991, JERS-1 1992, 
RADARSAT 1995), interest in SAR will increase. This 
package acts as a tutorial in SAR for users who are 
new to SAR remote sensing. It serves as a tool to 
aid the expert user in SAR image measurement and 
interpretation. 
ACKNOWLEDGEMENTS 
The authors acknowledge the support of the Canada 
Centre for Remote Sensing, National Resources 
Canada, and the Ice Services Branch, Atmospheric En- 
vironment Service, Environment Canada. 
[1] Frost, V.S, et al., 1982. A model for radar images and its application to 
adaptive digital filtering of multiplicative noise, IEEE Tr. on PAMI, Vol. 4, 
No. 2, pp. 157-166, March 1982. 
[2] Guindon, B., M. Adair, 1992. Analytic formulation for spaceborne SAR 
image geocoding and “value-added” product generation using digital elevation 
data, Canadian Journal of Remote Sensing, 18(1):2-12. 
[3] He, D.C., Wang, L., 1989. Texture unit, texture spectrum and texture 
analysis, IGARRS'89, Vol. 5, pp. 2769-2772. 
[4] Kuan, D.T., et al., 1987. Adaptive restoration of images with speckle, 
IEEE ASSP, Vol. 35, No. 3, pp. 373-383, March 1987. 
[5] Lee, Jong-Sen, 1981. Speckle analysis and smoothing of synthetic 
aperture radar images, Computer Graphics and Image Processing, Vol. 17, pP. 
24-32, 1981. 
[6] Lopes, A., et al., 1990. Maximum a posteriori speckle filtering and first 
order texture models in SAR images, Proceedings of IGARRS'90, Vol. III, pp: 
2409-2412, Washington D.C., May 1990. 
[7] Zebker, H.A., Van Zyl, J.J., 1991. Imaging radar polarimetry: a review, 
Proceedings of the IEEE, Vol. 79, No. 11, November 1991. 
[8] Ramseier, Reneé O., Andrew Emmons, Bernard Armour, Caren Garrity, 
1993. Fusion of ERS-1 SAR and SSM/I Ice Data. Proceedings of the 2nd ERS-1 
Symposium, Hamberg Germany, October 11-14 1993. 
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