Investigation of Phase Congruency for SSA Data Reduction
Abstract
The space situational awareness (SSA) data reduction pipeline is well established and formulated from our innate understanding of CCDs, optical distortion, and viewing conditions. Proper photometric calibration includes dark frame collection as well as the time-consuming task of on-sky flats which can vary with sky brightness and availability. Proper astrometric calibration includes synchronization of all timing sources, proper mount modeling, and jitter reduction. Once calibrated, further data analysis can include streak reduction, PSF fitting, and star separation with such goals as aperture photometry or dim object characterization. The ideal SSA feature detection algorithm would separate statistically-distinct spatial regions of an image into background, objects, and stars without need for darks/flats. Computer vision feature detection techniques may offer this one-stop-shop approach to data reduction and analysis using just the raw frames. One such technique uses phase congruency: a measure of the strength of how well the Fourier components of an image are in-phase which correlates with features such as edges. We examine a phase congruency approach based upon contrast invariant multi-scale image decomposition using simulated and nighttime Raven data.
- Publication:
-
Advanced Maui Optical and Space Surveillance Technologies Conference
- Pub Date:
- September 2014
- Bibcode:
- 2014amos.confE.101S