John R. Schott. Remote sensing. The image chain approach. 2nd edition. 2007.
Much of the remote sensing literature is written by and for the applications specialists who are users of remotely sensed data. The remote sensing "science" is often neglected or given only cursory treatment because of the need to stress the principles of the application area (e.g. geography, geology, forestry). Those books that more directly address remote sensing as a discipline have tended to heavily emphasize either the optics and physics of remote sensing or the digital image processing aspects.
This book treats remote sensing as a continuous process, including energymatter interaction, radiation propagation, sensor characteristics and effects, image processing, data fusion, and data dissemination. The emphasis is on the tools and procedures required to extract information from remotely sensed data using the image chain approach.
This approach to remote sensing has evolved from over two decades of teaching remote sensing to undergraduate and graduate students and three decades of research and consulting on remote sensing problems for government and industry. That experience has often shown that individuals or organizations all too often focus on one aspect of the problem before considering the entire process. Usually this results in a great deal of time, effort, and expense to achieve only a small improvement, because all the effort was placed somewhere other than the weak link in the chain. As a result, the perspective on remote sensing presented here is to treat the process as a continuous flow and to study the underlying science to a level sufficient to understand the many constrictions that limit that flow of information to the eventual user.
Because the field of remote sensing is so large, I have chosen to limit the treatment to aerial and satellite imaging for earth observation. In addition, because the vast majority of remote sensing is done passively in the visible through the thermal infrared region, I have emphasized this area. Within this spectral region, the underlying science and techniques of quantitative radiometric image acquisition, image analysis, and spectral image processing are emphasized. The details of specific sensors and software packages are downplayed because of their ephemeral nature, and photo interpretation and photogrammetry are only briefly introduced because of their thorough treatment elsewhere.
In writing, I've always had two audiences in mind. The first is the traditional student. As a text, this book is aimed at graduate students in the physical or engineering sciences taking a first course in remote sensing. It would also be appropriate for advanced undergraduates or as a second course for students in applications disciplines. In several cases, where the mathematical principles may be beyond what students in a particular discipline are required to know (e.g., 2-D linear systems theory), I have at tempted to use more extensive graphical and image examples to provide a conceptual understanding. I have assumed a working knowledge of university physics and calculus. In addition, parts of chapters 8 and 13 draw on linear systems theory, although these sections can be treated in a more descriptive fashion when necessary.
The second audience I had in mind when writing is the large number of scientists and engineers who were trained in the traditional disciplines and find themselves working in the remote sensing field. Having worked extensively with many of these scientists in government and industry, I wanted to compile a book that could be used to understand and begin to address many of the questions these individuals must first ask of the remote sensing field. I hope both of these groups will find this a useful tool box for working the problems ahead.
Historical perspective and photo mensuration.
Radiometry and radiation propagatiom.
The governing equation for radiance reaching the sensor.
Imaging sensors and instrument calibration.
Atmospheric compensation: solutions to the governing equation.
Digital image processing principles.
Multispectral remote sensing algorithms: land cover classification.
Spectroscopic image analysis.
Use of physics-based models to support spectral image analysis algorithms.
Image/data combination and information dissemination.