This document discusses hyperspectral remote sensing. It begins by explaining that hyperspectral sensors collect data across a large number of narrow bands, capturing details of the electromagnetic spectrum. It then discusses spectroscopy and how instruments like spectrometers are used to collect and analyze spectral data. The rest of the document details specific hyperspectral sensors like AVIRIS, how hyperspectral image cubes are represented, techniques for analyzing mixed pixel spectra through comparison to spectral libraries, and methods for identifying endmembers through geometric analysis of pixel clouds.

1. Hyperspectral Remote Sensing
Danny M. Vaughn, Ph.D., CMS
Hyperspectral
 Based upon a narrower and larger number of bands
representing the EMS.
 May include up to 200 bands at 10 nm (1/1,000,000,000
m) increments in wave length & at 10 – 12 bits (1024 –
4096 digital numbers).

2. Spectroscopy
 A detailed examination of measurable spectral data.
 Neuton – separated visible spectra through glass prisms.
 Dark lines (absorptive spectra) are observed as radiation
passes through gases at low temperatures.
 Light lines (emission spectra) occur from heated gases
(chemical elements) emitted from a radiating body (the sun,
earth, etc).

3.  Spectroscopes, spectrometers, spectrographs collect radiation
& divide it into spectra regions (using diffraction gratings &
prisms).
 Values are measured on film sensitive emulsions or
electronically.
 Allows for a more definitive identification of surface features.
 Data classified by coarse spectral resolution sensors (MSS,
TM, SPOT) may be corrected by hyperspectral libraries.

4. Hyperspectral Remote Sensing
 Incorporates imaging spectrometry (reflectance values off
earth surfaces).
 Libraries of spectral characteristics are more fine-tuned at
narrower wavelengths.
 Spectral responses of earth surface features are compared to
these spectral libraries.
.

5. AVIRIS – airborne visible-infrared imaging spectrometer.
 Initially developed by NASA (JPL, early 1980’s) as airborne
imaging spectrometer AIS).
 128 spectral bands.
 10 nm wide bands.
 Within an interval of 1.2 – 2.4 m (1.9 – 2.4m for geologic
investigations).
 8 m spatial resolution.
 AVIRS – Tested in 1987, placed in service in 1989.

6.  Hyperspectral sensors.
 Collect reflected and emitted radiation through objective
lens.
 Collimating lens projects the radiation as a beam of
parallel rays through diffraction grading.
 The grading separates the radiation into discrete bands.
 Energy in each band is detected by linear arrays of silicon
and indium antimonide.

7.  Sensors are configured into four panels: 0.4 – 0.7 m ;
0.7 – 1.3 m ; 1.3 – 1.9 m ; & 1.8 – 2.8 m.
 Spectral range is 0.4 – 2.45 m (400 2500nm)
 (1m = 106
m)
 (1nm = 109
m)
 Initially, 224 spectral bands each @ 10 nm wide. Final
processing yields 210 bands.
 Image size is 11 x 11 km at 20 m spatial resolution.

8. The Image Cube
 A 3-D representation of spectral values for a surface through
N number of hyperspectral bands.
 The top of the cube contains the shortest wavelength (0.4
m), while the bottom represents the longest wavelength (2.5
m).

9. Data Libraries
 Spectra for earth surface materials.
 Measurements are taken for a wide range of illumination
conditions.
 Each spectral record is linked to a metafile.
 Specific meteorological conditions, the nature of the surface,
& other ancillary information are recorded.

10. Spectral Matching
 Begins with acquisition & preprocessing techniques.
 Remove systematic errors & accurate calibration.
 Correct for atmospheric effects.
 Create color composites.
 ALARM to mark a specific pixel or region & return
associated regions with the same spectral patterns.

11. Spectral Mixing Analysis
 Surface features are often composed of multiple spectral
values.
 A sensor observes composite spectra that are not a clear
representation of the specific spectral properties of a
homogeneous feature.
 Pixel confusion exists from atmospheric effects, topographic
variations, & shadowing.
 Mixed pixel spectra occurs from spectral patterns too fine to
be resolved by a sensor.
 Linear mixing – occurs within a pixel that is an additive
combination of materials with varied spectral values.

12.  If the radiation reflected or emitted from a material remains
separate before reaching the sensor, its proportion of radiance
with respect to the observed total radiance of the pixel may be
estimated (weighted mean, eq. 9.2: Pw = (Mi – Fi)/(Wi-Fi).
 This is most likely when multiple classes occur within
compact areas within the pixel.
 Nonlinear mixing – occurs when the spectral values of
several surface features mix in highly dispersed patterns.
 These are not separable by weighted mean techniques.

13.  Spectral mixing analysis – extracting pure spectra for mixed
pixels that exhibit spectral mixtures that are linear.
 Discrete spectral feature components may be identified from a
mixed pixel.
 Hyperspactral images are compared to spectral signatures of
surface materials derived under laboratory or controlled
measurement conditions.
 Key components of a scene (mineralogies of soils, rocks, or
other unconsolidated deposits may be identified in a pixel).
 Pure pixels are separated from those that are impure (mixed
classes).

14. Complex Geometry
 Examines muti-dimensional data (multiple bands)
 Individual pixels are examined for multiple linear
combinations of varied, yet pure spectra.
 The data must have greater dimensionality (more spectral
bands) that the number of pure components within a given
pixel.
 Define a geometry that is the simplest (simplex)
representation of a cloud of mixed pixels (three vertices
forming a triangle).

15.  This may be based upon three observations of pixels that define
the end members or extreme spatial limits of the triangular-
shaped cloud.
 The end members represent the pure pixels that contribute to
the varied spectra of the interior pixels.
 The interior pixels must be positive unit sum combinations of
the pure pixels, and must equal unity.
 A subset defining a region of relative homogeneity (low
spectral variance among mixed classes) is first defined.
 Radiance values are converted to ground level reflectances
using atmospheric models.

16.  The dimensionality of hyperspectral data is reduced through
principal components analysis.
 An idealized simplex triangle (observed approximation) is
identified (A, B, C, fig. 14.7) by the three vertices and
associated facets.
 An exterior surface (convex hull) is represented by an area
defined by the curvature forming the outer boundary of the
cloud.
 The idealized spectra is represented by another triangle with
facets forming an outer boundary A’, B’, C’.
 The spectral values of the end members are compared to
spectral libraries to identify the varied features contributing to
the observed spectra (mixed pixels) in the cloud.