Hyperspectral Data Processing: Algorithm Design and Analysis

1.4 Scope of This Book

While writing this book it is important to consider hyperspectral image processing and hyperspectral signal processing as two different research areas and treat them separately. When hyperspectral data are processed as image cubes, it is called hyperspectral image processing where data samples are image pixel vectors and both spectral and spatial correlation among image pixel vectors can be made available for data processing. On the other hand, when hyperspectral data are processed as signatures it is called hyperspectral signal processing where a signature is a one-dimensional signal, which represents its spectral profile over a range of wavelengths for signature characterization. In this case, only interband spectral correlation within the signature is available for data processing and no other information such as sample spatial or spectral correlation used in hyperspectral image processing is available for signature processing. Such hyperspectral signals include data obtained from laboratories, databases, and spectral libraries where no data sample spatial/spectral correlation is available. Therefore, techniques developed for hyperspectral image processing may not be directly applicable to hyperspectral signal processing and vice versa. Unfortunately, it seems that there is no concern in distinguishing one from another when it comes to algorithm design. This book is believed to be the first to do so by treating hyperspectral image processing and hyperspectral ...