Machine Learning for Background Estimation in Multispectral Imagery

Wednesday, October 24, 2018 - 4:10pm - 5:00pm
Keller 3-180
James Theiler (Los Alamos National Laboratory)
We can improve the detection of targets and anomalies in a cluttered background by more effectively estimating that background. With a good estimate of what the target-free radiance or reflectance ought to be at a pixel, we have a point of comparison with what the measured value of that pixel actually happens to be. It is common to make this estimate using the mean of pixels in an annulus around the pixel of interest. But there is more information in the annulus than this mean value, and one can derive more general estimators than just the mean. The key notion is multivariate regression of the central pixel against the pixels in the surrounding annulus. This can be done on a band-by-band basis, or with multiple bands simultaneously.