Roughness Measurements with Polychromatic Speckles on Tilted Surfaces

Surface light scattering enables contactless and fast measurements of surface roughness. A surface inclination alters the direction of the scattering beam and thus the measured surface roughness is calculated from the detected intensity distribution. Hence, an accurate sensor–surface alignment is necessary. In order to achieve tilt-independent roughness measurements, a model-based evaluation approach for polychromatic speckle patterns is presented. By evaluating the shape of the superposed speckles, which occur for polychromatic illumination, with regard to the distance to the scattering centre, surfaces with an Sa roughness value in the range of 0.8–3.2 μm are measurable. Experimental investigations demonstrate that the influence of a surface tilt up to ± 1.25° on the roughness measurement is reduced by 90%. As a result, the robustness of the polychromatic speckle roughness measurement is improved, which allows to speed up the adjustment of the measurement system or the surface sample, respectively.

Extraction of Peak Feature Based on Synthetic Sinc Model in SAR images

Peak is an important feature in Synthetic Aperture Radar(SAR), which represents essence of scattering centre. There are two general approaches in the literature to extract peak. One way is to extract peak after speckle suppression filtering. Using this method, the extracted feature is in accurate, and the algorithm is more complicated. Another is that detecting the amplitude of the peak directly. In order to have a fast and accurate peak extraction, we proposed using the Sinc peak model algorithm in this paper. It directly extracts peak features from the original SAR image without any noise suppression filtering, which is instead of Gauss mask function. The estimation parameters of peaks use QE theorists. Finally, we can get accuracy three parameters to describe peak features. Analytic fluctuation of parameters is compared with Gauss model peaks using truth SAR images. Experimental demonstrate that the new algorithm is more effective than others for extracting peak features in SAR images.