Introduction to Imaging Spectroscopy

part 2

Remote Sensing (GRS-20306)

Outline

Part 1

Definition

History

Why spectroscopy works!

Measurement methods

● Non-imaging

● Imaging

Applications

Part 2

Analytical Methods

● SAM

● SUM

Exercise

● Cuprite

Its all in the mix!

Pixel by definition mixed

Identification: pixel size vs. feature of interest

Spatial distribution lost

Endmember 0.8

0.6

0.4

0.2

0.0

Re

fle

cta

nce

[sca

led

fro

m 0

-1]

24002200200018001600140012001000800600400

Wavelength [nm]

0.8

0.6

0.4

0.2

0.0

Kaolinite Dolomite Hematite

Kaolinite Absorption Feature

Dolomite Absorption Feature

Hematite Absorption Feature

Kaolinite Absorption Feature

What causes spectral mixing

A variety of factors interact to produce the signal received by the imaging spectrometer:

● A very thin volume of material interacts with incident

sunlight. All the materials present in this volume

contribute to the total reflected signal.

● Spatial mixing of materials in the area represented by a

single pixel results in spectrally mixed reflected signals.

● Variable illumination due to topography (shade) and

actual shadow in the area represented by the pixel

further modify the reflected signal, basically mixing

with a black endmember.

● The imaging spectrometer integrates the reflected light

from each pixel.

Analytical Mapping Methods in Imaging Spectroscopy

Maximum Likelihood Classification (MLC)

Spectral Angle Mapper (SAM)

● http://www.ittvis.com/portals/0/tutorials/envi/Mapping_Methods.pdf

Spectral Unmixing (SUM)

● http://www.ittvis.com/portals/0/tutorials/envi/Adv_Hyperspectral_Analysis.pdf

Spectral Feature Fitting (SFF)

Mixture-Tuned Matched Filtering (MTMF)

RS software: ERDAS-IMAGINE and IDL-ENVI (and R)

Endmember selection

Training areas (MLC) vs. endmembers

Selection of endmembers:

● Spectral libraries

● Field radiometry

● Image spectra

● Field information

● Automated

methods: PPI

Spectral Angle Mapper: principle

automated method for comparing image spectra to selected

endmember spectra

assumes that data have been reduced to apparent reflectance

(true reflectance multiplied by some unknown gain factor, controlled

by topography and shadows)

determines similarity between image spectrum and reference

spectrum by calculating the spectral angle in n-D space (see

next)

SAM calculates angle map (in radians) per endmember: a new

data cube is prepared for nr of selected bands

Gray-level thresholding is typically used to empirically

determine areas that most closely match the reference

spectrum while retaining spatial coherence.

Spectral Angle Mapper: method

Consider a reference spectrum and an pixel spectrum from two-band data. The two different materials are represented in a 2D scatter plot by a point for each given illumination, or as a line (vector) for all possible illuminations.

t: pixel spectrum r: reference spectrum nb: number of bands

Spectral Angle Mapper: example

Using hyperspectral plant signatures for CO2 leak detection (Male et al., 2010)

Spectral UnMixing: principle

The linear mixing model assumes no interaction between materials. If each photon only sees one material, these signals add (a linear process). Multiple scattering involving several materials can be thought of as cascaded multiplications (a non-linear process).

The spatial scale of the mixing and the physical distribution of the materials govern the degree of non-linearity.

Large-scale aerial mixing is very linear.

Small-scale intimate mixtures are slightly non-linear. In most cases, the non-linear mixing is a second-order effect. Many surface materials mix in non-linear fashions, but approximations of linear unmixing techniques appear to work well in many circumstances (Boardman and Kruse, 1994).

Using linear methods to estimate material abundance is not as accurate as using non-linear techniques, but to the first order, they adequately represent conditions at the surface.

Spectral UnMixing: method

Each endmember has a unique spectrum

IFOV of pixel

A

B

C

A

B

C

A single pixel with three materials A, B and C

Material Fraction

0.25

0.25

0.50

The mixed spectrum is just a weighted average

mix=0.25*A+0.25*B+0.5*C

Spectral UnMixing: method

Mathematics of Linear Unmixing

Ri = reflectance of the mixed spectrum of a pixel

in image band i

j = fraction of end-member j

Reij = reflectance of the end-member spectrum j in band i

i = the residual error

n = number of end-members

Constraining assumptions: and

iij

n

j

ji fR

Re1

11

n

j

jf 10 jf

Cuprite, Nevada

Cuprite, Nevada (USA) is one of the most frequently used test-site for remote sensing instrument validation

Cuprite is mineral of the class ‘Oxides and Hydroxides’ and its chemical formula is Copper Oxide (Cu2O). Cuprite is a major ore of Copper and is still actively mined

Cuprite

A Real World Example

Mineral deposits.

Provide resources for

modern society

Possible sources of

life

Possible sources of

acidic water

Cuprite, Nevada is

an ancient

hydrothermal

alteration system

(like Yellowstone)

Cuprite Mapping

Cuprite 3D View

Landsat TM (Cuprite)

Landsat TM band 5 at 1650 nm

GER 63 channel data (Cuprite)

GERIS band 43 at 2216.10 nm

AVIRIS 1995 (Cuprite)

AVIRIS band 194 at 2210.8 nm

Cuprite - Spectral Unmixing

Alunite Calcite Kaolinite Silica Zeolite

RMS image

Geologic map from unmixing

Mars: Mineral Mapping with OMEGA

Regional map of

Syrtis Major

region showing

regions enriched

in olivine, High

Calcium Pyroxene

(HCP) and Low

Calcium Pyroxene

(LCP). Results

draped over

MOLA shaded

relief (Mustard et

al., Science,

2005)

Fingerprint

The spectrum of each material produces a “fingerprint” which allows it to be identified

Tetracorder identifies multiple materials, including effects of mixtures, grain size, and coatings.

Spectral endmembers of heathland habitats

Source: Mücher, Kooistra, et al., 2012 Ecological Indicators

SUM of heathland habitats

Summary

Mixed pixels: homogeneity at RS pixel level is a rare phenomenon at the Earth surface

SAM and SUM use high spectral dimension of IS to map surface components

Identification of relative concentrations for mixed pixels including spatial distribution

Proper endmember selection is crucial for accurate results