Hyperspectral Satellites and System Design

Shen-En Qian

• Publisher: CRC Press
• Year: 2020
• ISBN:
  9780429561177
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Mechanical Engineering
• Index: Technology & Engineering

Hyperspectral Satellites and System Design is the first book on this subject. It provides a systematic analysis and detailed design of the entire development process of hyperspectral satellites. Derived from the author's 25-year firsthand experience as a technical lead of space missions at the Canadian Space Agency, the book offers engineers, scientists, and decision-makers detailed knowledge and guidelines on hyperspectral satellite system design, trade-offs, performance modeling and simulation, optimization from component to system level, subsystem design, and implementation strategies. This information will help reduce the risk, shorten the development period, and lower the cost of hyperspectral satellite missions.

This book is a must-have reference for professionals in developing hyperspectral satellites and data applications. It is also an excellent introductory book for early practitioners and students who want to learn more about hyperspectral satellites and their applications.

1Introduction to Hyperspectral Satellites

1.1Spaceborne Spectroscopy and Imaging

While multispectral satellites, such as Landsat and SPOT satellites, have been in regular use since the 1970s, hyperspectral satellites have emerged as a new generation of remote sensing satellites since the beginning of this millennium. Imaging spectrometry, also known as hyperspectral imaging, is a combination of the traditional spectroscopy technology and the modern imaging system (Qian 2013).

Spectroscopy is a key analytical method used to investigate material composition and related processes through the study of the interaction of light with matter. Energy is absorbed by the matter, creating an excited state. The interaction creates some form of electromagnetic waves. Isaac Newton described the spectral nature of light. Spectroscopy or spectrometry deals with the measurement of a specific spectrum for identification of matters. By using a spectrometer, one can determine the level of excitement in the matter's atoms to identify what kind of material it is. It is incredibly difficult to make the distinction just with the naked eye, as human eyes are not able to see the fine details. Joseph von Fraunhofer invented the spectroscope in 1814 and used it to characterize the optical properties of glass for the development of more powerful telescopes. He also identified the dark lines in the solar spectrum. Kirchhoff and Bunsen used spectroscopy to investigate the composition of the solar atmosphere by establishing the connection between the solar Fraunhofer lines and the spectroscopic signatures of elements observed in the laboratory. Determining composition remotely, without physical contact, is one of the most valuable capabilities of spectroscopy. Since its beginning, spectroscopy has evolved and been used to enable a broad range of scientific discoveries by Edwin Hubble to deduce the expanding nature of the universe.

Spectroscopy is widely used in laboratories in the disciplines of physics, chemistry, and biology to investigate material properties. Spectroscopic data are often represented by an emission spectrum, a plot of the response of interest as a function of wavelength or frequency. For example, colorimetry used for the investigation of cholesterol or blood sugar in chemical laboratories is a form of spectroscopy. Spectrometry has also been used for determining blood alcohol levels, checking automobile emissions, and monitoring smokestack pollution.

A modern imaging system converts the visual characteristics of an object, such as a physical scene or the interior structure of an object, into digital signals and creates digitally encoded representations that are processed by a processor or a computer and give output in the form of a digital image. Imaging systems typically consist of a camera and an imaging lens, along with an illumination source. Depending on the system setup, imaging systems magnify or enhance observed objects to ease the viewing or inspection of small or unclear details. Computers are becoming more and more powerful with increasing capacities for running programs of any kind, especially digital imaging software. Software is becoming both smarter and simpler at a fast pace.

In the late 1970s, detector, optical, and computer technologies advanced sufficiently to enable a combination of spectroscopy with an imaging system. The combination of the spectroscopy technology and the modern imaging system is referred to as imaging spectrometry, now also called hyperspectral imaging. It could measure a spectrum for every pixel in an image. This provides revolutionary ways of observing the Earth by collecting information of each pixel in a scene across the electromagnetic spectrum. Imaging spectroscopy operating in the solar-reflected spectrum senses objects on the ground in detail spectrally and spatially. Molecules and particles of the land, water and atmosphere interact with solar energy in the 400–2500 nm spectral region through absorption, reflection, and scattering processes. Imaging spectrometers in the solar-reflected spectrum measure spectra of the ground objects as images in some or the entire portion of the spectra. These spectral measurements are used to determine constituent composition through the physics and chemistry of spectroscopy for scientific research and applications over the regional scale of the image.

The primary advantage of hyperspectral imaging is that, because an entire spectrum is acquired for each pixel of the acquired imagery, an operator needs no prior knowledge of the sample, and post-processing allows all available information from the dataset to be exploited (Chang 2013). Hyperspectral imaging can also take advantage of the spatial relationships among the different spectra in a neighborhood, allowing more elaborate spectral-spatial models for a more accurate segmentation and classification of the image (Grahn and Geladi 2007). The story of hyperspectral imaging is closely tied to advances in digital electronics and computing capabilities due to its complexity for acquiring and processing large data volume.

The term hyperspectral imaging was originally defined by Goetz et al. (1985) as “the acquisition of images in hundreds of contiguous, registered spectral bands such that for each pixel a radiance spectrum can be derived.” It sets this type of spectral remote sensing apart from multispectral imaging by requiring the spectral bands to be contiguous, so that no gaps occur through which precious spectral information might slip undetected. This original definition, however, did not explicitly mention the bandwidth of the spectral bands with respect to that of multispectral imaging. It did implicitly define the bandwidth of the hyperspectral bands as being much narrower, because of hundreds of bands in hyperspectral imaging within the same wavelength range rather than a few bands in multispectral imaging. The bandwidth of hyperspectral sensors is typically 10 nm or less, which is much narrower than that of multispectral sensors whose bandwidth is about 100 nm or so. The narrowness of the spectral bands in hyperspectral imaging is more important than the number of spectral bands being in hundreds. An imaging sensor that produces spectral images with a bandwidth of 10 nm or less should be classified as a hyperspectral sensor even if its total number of bands is less than a hundred. The original definition of hyperspectral imaging could be updated as “the acquisition of many images of contiguous, narrow, registered spectral bands such that for each pixel a radiance spectrum can be derived” to explicitly mention the bandwidth of the spectral bands.

Figure 1.1 shows the concept principle of a hyperspectral satellite. It acquires images of a given scene on the ground in hundreds (or tens sometimes) of continuous and narrow spectral bands over wavelengths that range from the near-ultraviolet to the shortwave infrared. Each image, often referred to as spectral image or band image, corresponds to a particular wavelength or spectral band number. The collected “datacube” contains both spatial and spectral information of the materials within the scene. Each element or pixel in the scene is sampled across hundreds (or tens) of narrow band images at a particular spatial location in the datacube, resulting in a one-dimensional (1D) spectrum. It is a plot of wavelength versus radiance or reflectance. The spectrum for a single pixel acquired by a hyperspectral satellite appears similar to a spectrum collected by a spectrometer in a spectroscopy laboratory. The spectrum can be used to identify ...