Hyperspectral Data Processing: Algorithm Design and Analysis

33.9 Further Topics

It is nearly impossible to write a book to cover all interesting topics as it was originally planned. This is certainly the case for this book. In particular, real-time hyperspectral image processing has become an emerging and booming area in recent years, and many research efforts have been reported and published in the literature on a fast pace track. The following sections only provide a preview of this topic. More details can be found in Chang (2013).

33.9.1 Causal Processing

Causal processing is a prerequisite to real-time processing. It only uses the data samples that were already visited in the past but not those in the future for data processing. With this content, spatial domain-based literal techniques in traditional image processing are generally not causal because they are primarily developed to take advantage of spatial correlation among data samples. For example, texture-based and window-based image processing techniques are usually not applicable to real-time processing. This is also true for most anomaly detection techniques such as the commonly used anomaly detector, RX detector (Reed and Yu, 1990), and CEM detector (Harsanyi, 1993) which are not real-time detectors because they require the entire dataset to compute a global covariance or correlation matrix prior to detection. By contrast, the pixel-based nonliteral techniques developed for hyperspectral imagery are actually causal processing techniques due to the fact that no interpixel spatial ...