Spatial aliasing quantification and analysis of existing imaging sensors: NASA’s Hubble Space Telescope, OrbView-3 OHRIS, and commercial-off-the-shelf camera for autonomous robots

Commentary: aliasing, imaging, and money

“…It occurred to me after writing two articles 1 , 2 on aliasing, and its quantification for optical imaging systems in particular, that aliasing in general is somewhat of a mystery to many. I surmise this is because the mathematics is brought up early on and well before the issue is properly appreciated.…”

“…In the initial half of this commentary, I aim to dispel the confusion and explain aliasing generically using a commonplace but exaggerated example since discretizing or sampling is virtually everywhere and aliasing is its shadow. While I used optical imaging as the vehicle on how to quantify aliasing generally, 1 , 2 and whether you are enthused by optical imaging or sciences in general or neither, my objective is to explain what aliasing is and why it is so important in life without using mathematical equations. In the latter half of the commentary, examples are provided to show how aliasing error plays a role in the digital imaging chain with just a couple equations to provide finer details that are not referenceable.…”

“…If a sample is taken on the 15th of every month, we are attempting to capture what is routinely called the first fundamental frequency 3 Fourier component, which has a frequency of 130 days−1. When the 15th is not sampled, the presumption is the account has nominally $100,000 on the 15th (first fundamental frequency Fourier component folds over and masquerades as the zeroth frequency component), but we know this to be false or disinformation 1 , 2 because on the 15th there is only $1. If the loan officer included the 15th, the average balance would be more like $50,000.…”