Visualization using rational morphology and zonal magnitude reduction

Morphological filters are investigated and employed for detecting and visualizing objects within an image. The techniques developed here will be employed on NASA's Earth Observing System (EOS) satellite data products for the purpose of anomaly detection. Previous efforts have shown the phase information in the spectral domain to be more significant than the magnitude information in representing the location of objects in an image. The magnitude information does provide some useful information for object location, but it is also sensitive to image illumination, blurring, and magnification variations, all of which influence the performance of object detection algorithms. Magnitude reduction techniques in the spectral domain can dramatically improve subsequent object detection methods by causing them to rely less on the magnitude and more on the phase information of the image. However, magnitude reduction enhances the high-frequency noise within an image, often causing unwanted noise to be interpreted as image objects. We propose three new techniques for improved object detection and noise reduction. Our first method employs varying magnitude reductions within radially concentric zones, using increasingly greater reductions in higher frequency zones. By employing this zonal magnitude- reduction technique, we manage to attenuate the high-frequency noise component while still maintaining the improved visualization performance of the magnitude reduction method. Our second technique operates by utilizing several magnitude reductions of varying scale, performing object detection on each magnitude-reduced image, and combining the results for improved accuracy. This result-averaging method allows us to further reduce our false-alarm rate from high-frequency noise while increasing visualization performance. Our third method is a new technique which is based on the ratios of morphological filters. By combining classical morphological filters in this way, we are able to produce more robust results which can yield useful information as to the location of image objects.