Proceedings Article | 19 May 2005
KEYWORDS: RGB color model, Statistical modeling, Electro optical modeling, Principal component analysis, Data modeling, Statistical analysis, Cameras, Synthetic aperture radar, Error analysis, Sensors
We present a novel methodology for evaluating statistically predicted versus measured multi-modal imagery, such as Synthetic Aperture Radar (SAR), Electro-Optical (EO), Multi-Spectral (MS) and Hyper-Spectral (HS) modalities. While several scene modeling approaches have been proposed in the past for multi-modal image predictions, the problem of evaluating synthetic and measured images has remained an open issue. Although analytical prediction models would be appropriate for accuracy evaluations of man-made objects, for example, SAR target modeling based on Xpatch, the analytical models cannot be applied to prediction evaluation of natural scenes because of their randomness and high geometrical complexity imaged by any of the aforementioned sensor modality. Thus, statistical prediction models are frequently chosen as more appropriate scene modeling approaches and there is a need to evaluate the accuracy of statistically predicted versus measured imagery. This problem poses challenges in terms of selecting quantitative and qualitative evaluation techniques, and establishing a methodology for systematic comparisons of synthetic and measured images. In this work, we demonstrate clutter accuracy evaluations for modified measured and predicted synthetic images with statistically modeled clutter. We show experimental results for color (red, green and blue) and HS imaging modalities, and for statistical clutter models using Johnson's family of probability distribution functions (PDFs). The methodology includes several evaluation techniques for comparing image samples and their similarity, image histograms, statistical central moments, and estimated probability distribution functions (PDFs). Particularly, we assess correlation, histogram, chi-squared, pixel and PDF parameter based error metrics quantitatively, and relate them to a human visual perception of predicted image quality. The work is directly applicable to multi-sensor phenomenology modeling for exploitation, recognition and identification.
