Hyperspectral Data Processing: Algorithm Design and Analysis

4.1 Introduction

When a new algorithm is designed and developed, a frequently asked question is that “How does it perform compared to other algorithms?”. In other words, if one walks in with a new algorithm saying that his algorithm performs better than other existing algorithms, how do we substantiate his claim? This is particularly true for a new area such as hyperspectral imaging where new algorithms keep emerging and popping up in a fast pace and each algorithm claims to be better than others. Many users have been struggling and wrestling this issue when they come to select candidate algorithms for hardware architecture design and development such as field programmable gate array (FPGA). Despite the fact that computer-simulated data such as Monte Carlo simulations have been used for this purpose, on many occasions the computer simulations generally go far beyond reality and can be only used for proof-of-concept. In order to move to the next level, more realistic data are needed for further evaluation. The same dilemma occurs in medical community also where the so-called phantoms are designed and developed based on real data using controllable parameters to simulate real environments before experiments can be conducted for real data in vivo. One good example is the magnetic resonance (MR) brain web library provided by MR imaging simulator of McGill University, Montreal, Canada (available at www.bic.mni.mcgill.ca/brainweb/) (see Chapter 32). It seems natural that a similar approach ...