Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- 1 Introduction
- 2 The Remote Sensing Environment
- 3 Spectral Properties of Materials
- 4 Imaging Spectrometers
- 5 Imaging Spectrometer Characterization and Data Calibration
- 6 Radiative Transfer and Atmospheric Compensation
- 7 Statistical Models for Spectral Data
- 8 Linear Spectral Transformations
- 9 Spectral Mixture Analysis
- 10 Signal Detection Theory
- 11 Hyperspectral Data Exploitation
- Appendix Introduction to Gaussian Optics
- Bibliography
- Index
- Plate section
5 - Imaging Spectrometer Characterization and Data Calibration
Published online by Cambridge University Press: 10 November 2016
- Frontmatter
- Dedication
- Contents
- Preface
- 1 Introduction
- 2 The Remote Sensing Environment
- 3 Spectral Properties of Materials
- 4 Imaging Spectrometers
- 5 Imaging Spectrometer Characterization and Data Calibration
- 6 Radiative Transfer and Atmospheric Compensation
- 7 Statistical Models for Spectral Data
- 8 Linear Spectral Transformations
- 9 Spectral Mixture Analysis
- 10 Signal Detection Theory
- 11 Hyperspectral Data Exploitation
- Appendix Introduction to Gaussian Optics
- Bibliography
- Index
- Plate section
Summary
The utility of the data from an imaging spectrometer critically depends upon the quantitative relationship between the scene in nature and the scene as captured by the sensor. As was shown in Chapter 4, the raw data will only somewhat resemble the at-aperture spectral radiance from the surface due to the optical characteristics of the fore optic and the spectrometer. Additionally, the data acquisition by the focal plane array will further modify the irradiance that composes the image due to the spectral dependence of the detector material's quantum efficiency. It will also add noise terms that, if large enough, will further complicate the relationship between the scene and its raw image. The calibration of the data from an imaging spectrometer is the crucial data processing step that transforms the raw imagery into radiance that can be physically modeled. The science of radiometry provides the theoretical framework and the measurement processes that enable a sensor to be characterized and the data to be converted to physical units that are tied to reference standards. This chapter describes the process of sensor characterization that leads to calibration products that are applied to the raw data and some of the techniques that are used to evaluate the accuracy and precision of the calibrated data will be introduced. An overview of the important measurement and data reduction processes for vicarious calibration, which is critical for space-based systems, will also be presented.
Introduction
The characterization of an imaging spectrometer is challenging due to the spatial extent of the collected scene and the large spectral range that is relatively finely sampled, at least for an imager. For example, an Offner–Chrisp imaging spectrometer often has between 200 and 400 spectral and about 1000 spatial samples or about 400,000 individual measurements in a single readout of the focal plane array. To collect a scene the FPA is read out thousands of times. All of the data must be calibrated in order to be used to greatest effect, with the overarching goal being that the result should not depend upon the time or location of the collected scene, there should be no field of view dependence, and it should be immune, within reasonable limits, to the illumination and atmospheric conditions.
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- Chapter
- Information
- Hyperspectral Imaging Remote SensingPhysics, Sensors, and Algorithms, pp. 228 - 294Publisher: Cambridge University PressPrint publication year: 2016