Remote sensing involves the examination of features observed in several regions of the electromagnetic spectrum. Conventional remote sensing, as outlined in previous chapters, is based upon the use of several rather broadly defined spectral regions. Hyperspectral remote sensing is based upon the examination of many narrowly defined spectral channels.
HYPERSPECTRAL REMOTE SENSING
- Hyperspectral remote sensing detector system records hundreds of spectral bands of relatively narrow bandwidths (5-10 nm) simultaneously.
- With such detail, the ability to detect and identify unique trends in land and atmospheric data sets is greatly improved.
- Allows for a more specific analysis of land cover.
- Hyperspectral imaging belongs to a class of techniques commonly referred to as spectral imaging or spectral analysis.
- Hyperspectral imaging is the collecting and processing of information from across the electromagnetic spectrum.
- The human eye sees visible light in three bands, i.e. red, green, and blue whereas spectral imaging divides the spectrum into many more bands.
- Combination of imaging and spectroscopy in a single system: incorporates large data sets and requires new processing methods.
- Hyperspectral remote sensing is the science of acquiring digital imagery of earth materials in many narrow contiguous spectral bands.
- Hyperspectral remote sensing uses the practice of spectroscopy to examine images of the earth’s surface.
- The reflected or emitted radiation is measured at a fine spectral resolution to identify materials.
- Imaging spectrometry, imaging spectroscopy
- Hyperspectral (“too many,” “excessive”): 100s of bands
- Ultraspectral: 1000s of bands
Hyperspectral Image Analysis
- Hyperspectral image analysis techniques are derived using the field of spectroscopy which relate the molecular composition of a particular material with respect to the corresponding absorption and reflection pattern of light at individual wavelengths.
- Spectral information of known material can be collected in laboratory settings and stored as “libraries”. Different methods can be employed to compare the reference spectra with the obtained spectral reflectance.
- Another approach is spectrum rationing which is dividing every reflectance value in the reference spectrum by the respective value of the image spectrum.
One of the first airborne hyperspectral sensors was designed in the early 1980s by the Jet Propulsion Laboratory. The airborne imaging spectrometer greatly extended the scope of remote sensing by virtue of its number of spectral bands; their spatial, spectral, and radiometric details, and the accuracy of its calibration. AIS collected 128 spectral channels, each about 10 nm wide in the interval 1.2 to 2.4 um. The term hyperspectral remote recognizes the fundamental difference between these data and those of the usual broad-band remote sensing instruments.
Hyperspectral sensors necessarily employ designs that differ from those of the usual sensor systems. An objective lens collects radiation reflected or emitted from the scene; a collimating lens then projects the radiation as a beam of parallel rays through a diffraction grating that separates the radiation into discrete spectral bands.
Hyperspectral Image Cube
- The image cube refers to the representation of hyperspectral data as a three-dimensional figure, with two dimensions formed by the x and y axes of the usual map or image display and the third (z) formed by the accumulation of spectral data as additional bands are superimposed on each other.
- For E.x. following map showing the top of the cube is an image composed of data collected at the shortest wavelength and the bottom of the cube is an image composed of data collected at the longest wavelength. Intermediate wavelengths are found as slices through the cube at intermediate positions. Values for a single pixel observed along the edge of the cube form a spectral trace describing the spectra of the surface represented by the pixel.
Multispectral vs Hyperspectral Remote Sensing
- Multispectral imagery generally refers to 3 to 10 bands that are represented in pixels. Each band is acquired using a remote sensing radiometer.
- Hyperspectral imagery consists of much narrower bands (10-20 nm). A hyperspectral image could have hundreds of thousands of bands. This uses an imaging spectrometer.
- Having a higher level of spectral detail in hyperspectral images gives the better capability to see the unseen.
For example, hyperspectral remote sensing can distinguish between 3 minerals because of its high spectral resolution.
- It also adds a level of complexity: 200 narrow bands can be difficult to work with at times.
- Hyperspectral and multispectral images have many real-world applications. For example, hyperspectral imagery has been used to map invasive species and help in mineral exploration.
- There are hundreds more applications in the fields of agriculture, ecology, oil and gas, oceanography, and atmospheric studies where multispectral and hyperspectral remote sensing are being used to better understand the world we live in.
|Separated Spectral bands||Does not have any Spectral gaps|
|Broad Band RS: Wider Band With (100nm)||Narrow Band RS: Narrow Band Width (10-20nm)|
|Coarser representation of Spectral Signature||A complete representation of Spectral Signature|
|Fewer problems with calibration||Radiometric and spectral calibration is time-consuming|
|Smaller data volumes||Larger data volumes|
Why Hyperspectral Remote Sensing
- Most of the earth’s surface materials have diagnostic absorption features in the 400nm to 2500nm range of the electromagnetic spectrum.
- These diagnostic features are typical of a very narrow spectral appearance.
- The surface material can be identified if the spectrum is sampled at a sufficiently high spectral resolution.
Though multispectral instruments can discriminate materials, hyperspectral imaging is required to actually identify materials.
Perhaps the most significant advantage of multispectral over hyperspectral image data is its greater accessibility due to the larger number of space-based multispectral sensors.
Limitations of Hyperspectral Data
very sensitive to noise
Difficult to interpret the spectral signatures of an “impure” pixel.
The need for calibration
- Because of the high spectral resolution of hyperspectral imaging. Fine atmospheric absorption features will be detected, which may be confused with the ground material being imaged.
The spectral reflectance of a mixed is generally the weighted average of the spectral response of the classes within it.
Applications of Hyperspectral Remote Sensing
- Atmosphere: water vapor, cloud properties, aerosols
- Ecology: chlorophyll, leaf water, cellulose, pigments, lignin
- Geology: mineral and soil types
- Coastal Waters: chlorophyll, phytoplankton, dissolved organic materials, suspended sediments
- Snow/Ice: snow cover fraction, grainsize, melting
- Biomass Burning: subpixel temperatures, smoke
- Commercial: mineral exploration, agriculture, and forest production