The current progress of remote sensing systems, based on airborne and spaceborne platforms and involving active and passive sensors, provides an unprecedented wealth of information about the Earth surface for environmental monitoring, sustainable resource management, disaster prevention, emergency response, and defense. In this framework, mathematical models for image processing and analysis play fundamental roles. Effectively exploiting the potential conveyed by the availability of remote sensing data requires automatic or semi-automatic techniques capable of suitably characterizing and extracting thematic information of interest while minimizing the need for user intervention. The current development of mathematical models and methods for image processing and computer vision allows multiple remote sensing information extraction problems to be addressed successfully, accurately, and efficiently. In this introductory chapter, first, general characteristics of sensors and systems for Earth observation are summarized to define the basic terminology that will be used consistently throughout the book. Remote sensing image acquisition through passive and active sensors on-board spaceborne and airborne platforms is recalled together with the basic concepts of spatial, spectral, temporal, and radiometric resolution. Then, an overview of the main families of mathematical models and methods within the scientific field of two-dimensional remote sensing image processing is presented. The overall structure and organization of the book are also described.