THEMATIC INFORMATION EXTRACTION – HYPERSPECTRAL IMAGE ANALYSIS Course: Special Topics in Remote Sensing & GIS Mirza Muhammad Waqar Contact: [email protected]

THEMATIC INFORMATION EXTRACTION – HYPERSPECTRAL

IMAGE ANALYSIS

Course: Special Topics in Remote Sensing & GIS

Mirza Muhammad WaqarContact:

[email protected]+92-21-34650765-79 EXT:2257

RG712

mailto:[email protected]

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

Visual Individual Band Examination Visual Examination of Color Composite Animation Statistical Individual Band Examination

Radiometric Calibration 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”

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 vs Multispectral

Hyperspectral Image Acquisition

Spectrometer

Hyperspectral Image Acquisition

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

Extraction of Information from Hyperspectral Data

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

Accuracy Assessment

Geometric Correction

Band Selection

Atmospheric Correction

End Member Selection

Classification SAM

State the nature of the information extraction problem

1. Specify the geographical ROI2. Define the classes or biological

materials of interestAcquire 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 resolution2. 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

Process hyperspectral data to extract thematic information

1. Subset the study area 2. Conduct initial image quality assessment:

1. Visual individual band examination2. Visual examination of color composite

images3. 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 polishing5. Empirical Line Calibration

4. Geometric Correction / Rectification1. 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 Index2. N-dimensional end member visualization

7. Method of mapping and matching using hyperspectral data:

1. Spectral Angle Mapper (SAM)2. Subpixel Classification (Linear Spectral

Mixing3. Spectroscopic library matching

techniques4. Matched filter or mixture-tuned matched

filter5. Indices developed for use with

hyperspectral data6. Derivative spectroscopy

Perform accuracy assessment1. Select method

1. Qualitative confidence building2. Statistical measurement

2. Determine number of observations required by class

3. Select sampling scheme4. Obtain ground reference data5. Create and analyze error matrix:

1. Uni-varitae and multivariate statistical analysis

Accept or reject previously stated hypothesis.

Distribute result if accuracy is acceptable.

Initial Image Quality Assessment

To assess the data quality, suitable distortion measures relevant to end-user applications are required.

1. Visual Individual Band Examination2. Visual Examination of Color Composite Images 3. Animation4. 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/S0169-7439(03)00030-3. (http://www.sciencedirect.com/science/article/pii/S0169743903000303) Keywords: Multivariate Image Analysis (MIA); Multi-way 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 the hyperspectral data

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 quantitatively comparable with the in situ spectra 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. Water vapour, H2O

2. Carbon Dioxide, CO2

3. Ozone, O3

4. Nitrous Oxide, N2O

5. Methane, CH4

6. Carbon Monoxide, CO

7. 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.

1. Flat Field Correction

2. Internal Average Relative Reflectance (IARR)

3. Empirical Line Calibration

Selected Atmospheric Correction Models

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Flat Field Correction

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.

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Internal Average Relative Reflectance (IARR)

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.

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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).

Internal Average Relative Reflectance (IARR)

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Empirical Line Calibration

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

1. Principal Component Transformation

2. Minimum Noise Fraction Transformation (MNF)

Reducing the Dimensionality of Hyperspectral Data

Data Dimensionality

The number of spectral bands associated with a remote sensing system is referred to as its data dimensionality.

Orbview: 4 bands Landsat: 7 bands Worldview: 8 bands MODIS: 36 bands AVIRIS: 224 bands

Number of Bands

Com

plex

ity

/ Pr

oces

sing

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.

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Minimum Noise Fraction Transformation (MNF)

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.

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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.


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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


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 contain useful

information Noise dominated MNF eigenimages



Both the eignvalues and output eignimages are used to determine the true dimensionality of the data.

1. How many eignimages should we select for analysis?2. What should be the threshold for eignvalues?

MNF output bands that contain useful information usually have engine value greater than that of noisy

bands.


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Note

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.



1. Pixel Purity Index (PPI)

2. n-dimensional visualization of endmembers in feature space

Endmember Determination

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

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Pixel Purity Index Mapping (PPI)

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.

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Pixel Purity Index (PPI) to find the most spectrally pure (extreme) pixels in multispectral and hyperspectral images.

PPI is a rigorous mathematical method of determining the most spectrally pure pixels. 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




n-dimensional Endmember Visualization

Hyperspectral Mapping Method

Spectral Angle Mapper (SAM)

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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

Spectral feature fitting

Spectral Angle Mapping

Spectral Unmixing

Minral Maps

Questions & Discussion

Documents

THEMATIC INFORMATION EXTRACTION – HYPERSPECTRAL IMAGE ANALYSIS Course: Special Topics in Remote Sensing & GIS Mirza Muhammad Waqar Contact: [email protected]