Hyperspectral Data Processing: Algorithm Design and Analysis

1.5 Book's Organization

This book is organized in accordance with the order laid out by seven parts in three categories presented in the previous section. Each part can be read independently while keeping sufficient correlation with other parts.

1.5.1 Part I: Preliminaries

The preliminaries in Part I help readers grasp sufficient knowledge to follow this book without difficulty. It consists of six chapters.

Chapter 2 is Fundamentals of Subsample and Mixed Sample Analyses. It uses a simple example to illustrate issues of subsamples and mixed samples encountered in detection and classification. It then walks through various approaches using hard and soft decisions for subsample detection and mixed sample classification. It includes many techniques currently being used and available in the literature.

Chapter 3 introduces Three-Dimensional Receiver Operating Characteristics (3D ROC) Analysis that can be used as an evaluation tool for soft decision-making performance for hyperspectral target detection and classification. An ROC curve is defined as a curve plotted based on detection probability versus false alarm probability. An analysis that uses ROC curves to evaluate the effectiveness of a Neyman–Pearson detector is called ROC analysis. A major advantage of ROC analysis is that there is no need of specifying a particular cost function. For example, least squares error or signal-to-noise ratio may be a good criterion for detection of problems in signal processing and communications, ...