THEMATIC INFORMATION
EXTRACTION – HYPERSPECTRAL
IMAGE ANALYSIS
Mirza Muhammad Waqar
Contact:
mirza.waqar@ist.edu.pk
+92-21-34650765-79 EXT:2257
RG712
Course: Special Topics in Remote Sensing & GIS
Outlines




Imaging Spectrometry
Multispectral versus Hyperspectral
Hyperspectral Image Acquisition
Extraction of information from Hyperspectral data

Preprocessing of Data


Subset Study Area
Initial Image Quality Assessment





Radiometric Calibration







Visual Individual Band Examination
Visual Examination of Color Composite
Animation
Statistical Individual Band Examination
In situ data
Radiosounder
Radiative Transfer based Atmospheric Correction
Selected Atmospheric Correction Models
Reducing Data Redundancy
Endmember Determination
Hyperspectral Mapping Method
Imagining Spectrometry

Imagining spectrometry is defined as



the simultaneous acquisition of images in many relatively
narrow
contiguous and/or noncontiguous spectral bands
throughout the ultraviolet, visible, and infrared portions of
electromagnetic spectrum”
Hyperspectral vs Multispectral
Most multispectral
3 to 10 spectral bands
 For Example

Landsat (MSS, TM & ETM+)
 ALOS
 SPOT (HRV)
 IKNOS
 QuickBird
 Orbview
 Digital Globe
 Worldview
 Aerial phtography

Hyperspectral


At least 10 or more
spectral bands
Example includes:








MODIS
MERIS (Envisat)
MOS (IRS-3P, India)
Hyperion (EO-1)
CHRIS (PROBA, ESA)
AVIRIS (JPL, NASA)
DAIS 7915 (DLR)
HYDICE (NRL, USA)
Hyperspectral Image Acquisition
Spectrometer
Hyperspectral Image Acquisition
Extraction of Information from Hyperspectral Data
1.
Selection of appropriate Software Package
2.
Image Quality Assessment
3.
Radiometric Correction
4.
Geometric Correction
5.
Dimensionality Reduction
6.
Selection of end members
7.
Mapping methods
Selection of appropriate Software Package

The analysis of hyperspectral data usually required
selection of appropriate digital image processing software
package e.g.




ENVI, the Environment for Visualizing Images
ERDAS Imagine
IDRISI
PCI Geomatica
Geometric Correction
Band Selection
Atmospheric Correction
End Member Selection
Classification SAM
Accuracy Assessment
State the nature of the information extraction problem
1. Specify the geographical ROI
2. Define the classes or biological materials of interest
Acquire appropriate remote sensing and initial
ground ref data
1. Select RS data based on the following criteria
1. RS system consideration:
1. Spatial, spectral, temporal &
radiometric resolution
2. Environmental considerations:
1. Atmospheric, soil moisture,
phonological cycle, etc.
3. Obtain initial ground reference data based
on:
1. A priori knowledge of the study area
1.
2.
3.
4.
5.
Perform accuracy assessment
Select method
1. Qualitative confidence building
2. Statistical measurement
Determine number of observations required by
class
Select sampling scheme
Obtain ground reference data
Create and analyze error matrix:
1. Uni-varitae and multivariate statistical
analysis
Accept or reject previously stated hypothesis.
Distribute result if accuracy is acceptable.
Process hyperspectral data to extract thematic information
1. Subset the study area
2. Conduct initial image quality assessment:
1. Visual individual band examination
2. Visual examination of color composite images
3. Animation
4. Statistical individual band examination (S/N ratio)
3. Radiometric Correction
1. Collect necessary in situ spectroradiometer data (if
possible)
2. Collect in situ or environmental data (e.g. using
radio sounder)
3. Perform pixel by pixel correction (e.g. ACORN)
4. Perform pixel by pixel spectral polishing
5. Empirical Line Calibration
4. Geometric Correction / Rectification
1. Use onboard navigation and engineering data (GPS
& INS Data)
2. Nearest neighbor resampling
5. Reduce the dimensionality of hyperspectral data
1. Minimum Noise Fraction (MNF) transformatoin
6. End Member determination – locate pixels which relatively
pure spectral characteristics:
1. Pixel Purity Index
2. N-dimensional end member visualization
7. Method of mapping and matching using hyperspectral
data:
1. Spectral Angle Mapper (SAM)
2. Subpixel Classification (Linear Spectral Mixing
3. Spectroscopic library matching techniques
4. Matched filter or mixture-tuned matched filter
5. Indices developed for use with hyperspectral data
6. Derivative spectroscopy
Initial Image Quality Assessment

To assess the data quality, suitable distortion measures
relevant to end-user applications are required.
1.
2.
3.
4.
Visual Individual Band Examination
Visual Examination of Color Composite Images
Animation
Statistical Individual band Examination
1. Visual Individual Band Examination

Many bands of hyperspectral data contain
 bad
data values or they lie in the absorption
window.


Such bands must be excluded from the analysis
because these reduce the contrast of data.
Bad Data
 If
a band contain data values (e.g. -9999)
 Line dropout (an entire line has a value of -9999)
1. Visual Individual Band Examination
Reference: Jun Huang, Helle Wium, Karsten B. Qvist, Kim H. Esbensen, Multi-way methods in image analysis—relationships and applications,
Chemometrics and Intelligent Laboratory Systems, Volume 66, Issue 2, 28 June 2003, Pages 141-158, ISSN 0169-7439, 10.1016/S01697439(03)00030-3. (http://www.sciencedirect.com/science/article/pii/S0169743903000303) Keywords: Multivariate Image Analysis (MIA); Multiway methods; Unfolding; Image; PCA/PLS; PARAFAC; Tucker3; N-PLS; 2-D FFT
2. Visual Examination of Color Composites

Can be use to check
 The
individual bands are co-align
 Contain spectral information of value

Such examination provide
 Valuable
quantitative information
 About the individual scenes and bands in
hyperspectral data
the
2. Visual Examination of Color Composites
3. Animation

Most hyperspectral image analysis software
have image animation function.
 E.g.

every 5 second a new band will be displayed
Examination of hyperspectral data in this way
allows:
 Identify
individual bands that have serious
atmospheric attenuation
 Determine if any mis-registration of band exist
4. Statistical Individual Band Examination

It includes examination of uni-variate statistics of
individual band
Mean
 Median
 Mode
 Standard Deviation
 Range


To detection absorption feature

Noise level must be smaller than the absorption level.
Radiometric Calibration

To use hyperspectral data properly



It is generally accepted that the data must be
radiometrically corrected.
This process normally involves transforming the
hyperspectral data from at-sensor radiance, to
scaled surface reflectance.
This
allows
image
spectra
comparable with the in situ spectra

quantitatively
Obtained using handheld spectroradiometer.
Digital Number (DN)


Digital Number (DN) – the unitless integer that a
satellite uses to record relative amounts of
radiance (e.g. 0 – 255 where 0 = no radiance and
255 = some maximum amount of radiance that
the sensor is sensitive to). Each image pixel has
one DN for each band.
Note that DNs are just an index of radiance and
don’t have physical units of radiance.
Radiance

Radiance (L) – the physical amount of light
received at a particular place
 in
this case a satellite (watts/m2/sr).
Irradiance

Irradiance (E) – the amount of incoming light
from the sun (either at the ground (E) or at the
top of the atmosphere (E0 or TOA) (watts/m2).
Reflectance

Reflectance (r) – the amount of light that reflects off of
something divided by the amount of incoming light (often
given as a decimal fraction or a percent). Also called
surface reflectance
Apparent Reflectance (Albedo), Reflectance
Apparent Reflectance (Albedo)
 "Albedo is defined as the fraction of incident
radiation that is reflected by a surface.
Reflectance
 While reflectance is defined as this same fraction
for a single incidence angle, albedo is the
directional integration of reflectance over all
sun-view geometries."
1. In Situ Data Collection

For Hyperspectral Image Analysis,
 It
is always desirable to obtain handheld in situ
spectrometer measurement on the ground at
 Approximately same time as the remote sensing over
flight

Otherwise same time of day
 That cover the spectral range as the hyperspectral
imaging system

Spectrometer used in the field must be
calibrated with reference spectrometer
 (laboratory spectrometer)
2. Radiosondes

Radiosonders can provide valuable information
about the atmosphere
Tempearture
 Pressure
 Relative Humidity
 Wind Speed
 Ozone
 Wind Direction


Radiosonder data helps in radiometeric correction.
3. Radiative Transfer-based Atmospheric Correction

As atmosphere is variable through the scene.

Ideally, the analyst knows the exact nature of
atmospheric characteristics over each pixel.




Barometric Pressure
Water vapor
Amount of atmospheric molecules (Scattering)
Remotely sensing – derived radiance data in very
selective bands can be used

for pixel by pixel atmospheric correction.
Behaviour of Atmospheric Gases

Following seven gases do produce observable
absorption in the remotely sensed imagery.
1.
2.
3.
4.
5.
6.
7.
Water vapour, H2O
Carbon Dioxide, CO2
Ozone, O3
Nitrous Oxide, N2O
Methane, CH4
Carbon Monoxide, CO
Oxygen O2
Behaviour of Atmospheric Gases
Band-by-Band Spectral Polishing

Even after atmospheric correction there exist noise
in spectra
Which is due to sensor system anomilies
 Limited accuracy of

Standards
 Measurements
 Models
 Calibrations


Spectral polishing is used to remove such errors.

EFFORT (Empirical Flat Field Optimal Reflectance
Transformation)
Spectral Polishing - EFFORT
Input Parameters



Atmospherically corrected data
In situ spectroradiometer spectra
In situ spectral reflectance measurements are sometimes
referred to as “reality boost spectra”.
Note:
 Before applying EFFORT



Mask any invalid data from each band
Identify the bad bands and mask these from analysis
Avoid wavelength ranges that contain noise such as 1.4 µm and 1.9
µm water vapour absorption band.
Selected Atmospheric Correction Models
1.
2.
3.
Flat Field Correction
Internal Average Relative Reflectance (IARR)
Empirical Line Calibration
Flat Field Correction
32

The Flat Field Correction method normalizes
images
 to
an area of known “flat” reflectance (Goetz and
Srivastava, 1985; Roberts et al., 1986).

The average AVIRIS radiance spectrum from the
ROI is used as the reference spectrum, which is
then divided into the spectrum at each pixel of
the image.
Internal Average Relative Reflectance (IARR)
33



Used to convert raw DN values to relative
reflectance. This is done by dividing each pixel
spectrum by the overall average spectrum.
The IARR calibration method normalizes images to
a scene average spectrum.
Apparent reflectance is calculated for each pixel of
the image by dividing the reference spectrum into
the spectrum for each pixel.
Internal Average Relative Reflectance (IARR)
34

This is particularly effective for reducing
imaging spectrometer data to relative
reflectance
 in
an area where no ground measurements exist
and little is known about the scene (Kruse et al.,
1985; Kruse, 1988).
Empirical Line Calibration
35

The Empirical Line correction method forces
image data to match
 selected field
reflectance spectra (Roberts et al.,
1985; Conel et al., 1987; Kruse et al., 1990).

This method based on a model that is derived
 from
the regression of in situ spectroradiometer
measurements at specific location
For more detail read: Third Edition – Introductory Digital Image Processing: A Remote Sensing
Perspective by John R. Jensen Chapter 6
Geometric Correction
Geometric Correction of Hyperspectral Data
Reducing the Dimensionality of Hyperspectral Data
1.
2.
Principal Component Transformation
Minimum Noise Fraction Transformation (MNF)
Data Dimensionality
The number of spectral bands associated with a
remote sensing system is referred to as its data
dimensionality.
 Orbview:
 Landsat:
 Worldview:
 MODIS:
 AVIRIS:
4 bands
7 bands
8 bands
36 bands
224 bands
Complexity / Processing

Number of Bands
Data Dimensionality: Multispectral Data

Statistical measures
 Optimum
Index Factor (OIF)
 Principal Component Analysis (PCA)


These techniques have been used for data
dimensionality reduction for multispectral data.
These methods are not significant for reducing
hyperspectral data dimensionality.
Principal Component Transformation

Principal components analysis is a method in which
original data is transformed into a new set of data


which may better capture the essential information.
Often some variables are highly correlated
such that the information contained in one variable is
largely a duplication of the information contained in
another variable.
 Instead of throwing away the redundant data principal
components analysis condenses the information


in intercorrelated variables into a few variables, called
principal components.
Minimum Noise Fraction Transformation (MNF)
42



Hyperspectral Imaging generates vast volumes
of data.
100s or more bands might not be necessary to
identify and separate the surface materials of
interest to a particular study.
Furthermore, some bands might contain more
noise than others, making them more of a
detriment than an aid to the analysis.
Minimum Noise Fraction Transformation (MNF)
43

Eliminating noise and reducing the spectral
dimensionality of the data are the goals of
 Principal
Component Analysis (PCA)
 Minimum Noise Fraction Transformation (MNF).

Information
contained
in
individual
hyperspectral bands may be, in some regions of
the spectrum, highly redundant.
Minimum Noise Fraction Transformation (MNF)
44

The many redundant bands may be collapsed into a
much smaller set of MNF bands



without losing the critical information needed to
differentiate or identify surface materials.
Furthermore, the noise can also be identified and
eliminated using the same methods.
The MNF is used to determine the true or inherent
dimensionality of the hypserpsectral data.
To identify and segregate noise in the data
 To reduce the computation time

Minimum Noise Fraction Transformation (MNF)

The MNF applies two cascaded PCAs.
 First
transformation decorrelate and rescales noise
in the data
 Result
bands have unit variance and no band to band
correlation
 Second
transformation is a standardize PCA, this
results in
 Coherent
MNF eigenimages that
information
 Noise dominated MNF eigenimages
contain
useful
Minimum Noise Fraction Transformation (MNF)
Minimum Noise Fraction Transformation (MNF)

Both the eignvalues and output eignimages are
used to determine the true dimensionality of the
data.
1.
2.

How many eignimages should we select for analysis?
What should be the threshold for eignvalues?
MNF output bands that contain useful information

usually have engine value greater than that of noisy
bands.
Minimum Noise Fraction Transformation (MNF)
48
Note
49


MNF results are applicable to that particular
dataset or others with very consistent and
similar spectral characteristics.
If a project involves many hyperspectral images
collected over a large area, the MNF results from
one image or set of images may not apply to
others in the project.
Minimum Noise Fraction Transformation (MNF)
Minimum Noise Fraction Transformation (MNF)
Endmember Determination
1.
Pixel Purity Index (PPI)
2.
n-dimensional visualization of endmembers in feature space
Endmember Determination

The primary goal of most of hyperspectral analysis is to
identify the



Physical, Chemical
Properties of materials found within the IFOV of the sensor
system.
The major materials found within hyperspectral image
are called endmembers.

These represents relatively pure materials




Water
Asphalt
Concrete
Healthy grass
Pixel Purity Index Mapping (PPI)
54


Imagine how much more difficult it would be to
identify appropriate pixels or groups of pixels
with ideal hyperspectral signatures.
Use Pixel Purity Index (PPI) to find the most
spectrally pure (extreme) pixels in multispectral
and hyperspectral images.
Pixel Purity Index Mapping (PPI)
55


Pixel Purity Index (PPI) to find the most spectrally
pure (extreme) pixels in multispectral and
hyperspectral images.
PPI is a rigorous mathematical method
determining the most spectrally pure pixels.
of
By repeatedly projecting n-dimensional scatter plots of
the MNF images.
 PPI simply identify the most pure pixel.
 It is difficult to label the type of endmember at this stage

Pixel Purity Index Mapping (PPI)
Pixel Purity Index Mapping (PPI)
n-dimensional Endmember Visualization
Hyperspectral Mapping Method
Spectral Angle Mapper (SAM)
Hyperspectral Data Acquisition
Raw Radiance Data
Spectral Calibration
At-Sensor Spectrally Calibrated Radiance
Spatial Pre-Processing and Geocoding
Radiometrically and Spatially processed radiance image
Atmospheric Correction, solar irradiance correction
Geocoding reflectance image
Feature Mapping
Data analysis for feature mapping
Absorption band
characterization
61
Spectral feature
fitting
Minral Maps
Spectral Angle
Mapping
Spectral
Unmixing
Questions & Discussion