Influence of multiplicative noise variance evaluation accuracy of mm-band SLAR image filtering efficiency

Image filtering is a stage commonly implied in remote sensing data processing in order to remove noise. In this paper we pay attention to filtering the images formed by millimeter (Ka) band side look aperture radars (SLARs) that can serve as imaging subsystems of multipurpose airborne remote sensing complexes. For SLAR images the dominant factor degrading their quality is multiplicative noise that is characterized by probability density function close to Gaussian and relative variance. The goal of this paper is to study how the errors in multiplicative noise variance evaluation influence the performance of different filters with application to Ka-band SLAR image processing. We have considered two test images, one containing a lot of texture and details, and the other image has many homogeneous regions. If the test image contains a lot of details and texture, the optimal value is commonly less than for test image that contains a large percentage of homogeneous regions. The error of variance evaluation more strongly influences the performance of the modified sigma filter than the performance of the local statistic Lee and DCT-based filters. The performance of filters greatly depends upon considered test image, noise statistical characteristics, setting the filter parameters. The numerical simulation data presented serve as good background and motivation for the design of locally adaptive filters that perform hard or soft switching of several different filter outputs in order to make use of the advantages of these filters and to avoid their drawbacks.