Hyperspectral Remote Sensing in

Mineral Mapping

Presented by

J S S Vani

1

Contents:

• Introduction

• Hyperspectral Image Analysis for Mineral Mapping

• Literature review

• Case Study 1

• Case Study 2

• Summary

• References

2

Introduction:

• Classical mineral mapping utilize physical characteristics of rocks such

as mineralogy, weathering characteristics, geochemical signatures, to

determine the nature and distribution of geologic units.

• Subtle mineralogical differences, often important for making

distinctions between rock formations, are difficult to map.

• Hyperspectral remote sensing provides a unique means of remotely

mapping mineralogy.

3

Co ti ued…

• The asi o ept is all su sta es depe di g o their ole ular

composition scatter electromagnetic energy at specific wavelengths

i disti tive patter

• Minerals and rocks display certain analytic spectral characteristics

throughout the electromagnetic spectrum.

• These spectral characteristics allow their chemical composition and

relative abundance to be mapped.

4

Hyperspectral image analysis for

mineral mapping:

• A hyperspectral image is an image cube with spatial information in

X,Y and spectral in Z direction.

• A radiant energy value is recorded for each data point(pixel) in the

image for every wavelength sampled.

• As a result, data volume to be processed is generally huge and

computationally complex.

• In order to solve this problem, several approaches have been

developed for image processing and analysis.

5

6

Fig 1: Concept of Hyperspectral imagery

• The processing of hyperspectral imagery involves various

steps like:

– Data reduction techniques

• Radiometric corrections using algorithms like FLAASH, ARTEM,

HATCH etc.

• Minimum Noise Fraction (MNF)

• Pixel Purity Index (PPI)

– Image classification techniques

• Spectral Angle Mapper (SAM)

• n-Dimensional Visualizer

• Mixture Tuned Matched Filtering(MTMF)

7

• Minimum noise fraction transformation:

– Used to segregate noise in the data, and to reduce the computational

requirements for subsequent processing.

– This is a two step process:

• The first step results in transformed data in which the noise has unit

variance and no band-to-band correlations.

• The second step is a standard Principal Components Analysis.

• Pixel Purity Index:

– It is a ea s of fi di g the ost spe trall pure or e tre e pi els.

– A PPI image is created where each pixel value corresponds to the number

of times that pixel was recorded as extreme.

– The PPI is run on an MNF transform result, excluding the noise bands.

– The results of the PPI are used as input into n-D Visualiser.

8

• N-Dimensional visualiser:

– Used to further refine the selection of the most spectrally pure end

members from PPI result.

– Extreme pixels which ultimately correspond to end members can be

determined by rotating the scatter plot in n-dimensions.

– The selected classes will be exported to Region of Interest(ROI) and used

as input for further spectral processing.

• Spectral angle Mappper:

– It determines the similarity between

a pixel and each of the reference

spectra based on the calculation of

the spe tral a gle between them.

– Smaller angle means a closer match

between the two spectra and the

pixel is identified as the fixed class

Fig 2 Showing the SAM algorithm

9

• Mixture Tuned Matched Filtering:

– It is a special classification and unmixing technique for

identification of end members.

– Is a hybrid method based on the combination of the matched

filter method (no requirement to know all the endmembers)

and linear mixture theory.

10

Literature Review:

• Kruse(1998) suggested the measurement of the Earth’s

surface in hundreds of spectral bands, provides a unique

means of remotely mapping mineralogy.

• Perez,et.al (2000) used SAM Spectral Angle Mapper, MF

Matched Filtering, SFF Spectral Feature Fitting, MNF Minimum

Noise Fraction techniques for Mineral Mapping for Los

Menucos region.

11

• Sanjeevi (2008) analysed that spectral unmixing of

hyperspectral data may be combined with the terrain

parameters to identify mineral deposits and also to estimate

the quality of these deposits.

• Srivastav,et.al(2012) illustrated mineral abundance mapping

using MTMF Mixture-Tuned Matched Filtering technique.

Minerals identified were in accordance with the ground

lithology.

• Jibran Khan (2013),presented a preliminary methodology for

extraction of minerals by analysis of Hyerion data using ERDAS

Imagine software(Intergraph).

12

Case Study 1:

• Mineral Abundance mapping using Hyperion dataset in

Udaipur

• Author: Dr.S.K.Srivastav, Dr.Prabhakaran.

• Journal: 14th International Geospatial Conference,2012.

• Objective: (a)To understand EO-1 hyperion data processing

and spectral analysis for mineral abundance mapping in

the study area. (b)The study attempts to map the various

minerals present in the exposed rock surface in the study

area.

13

Study Area:

• The area is located southwest of Udaipur City, Rajasthan.

• The extent of the study area is from 73° 33’ 25 E to 73° 42’

53 E and 24° 09’ 34 N to 24° 31’ 40 N covering 303.43 sq

km.

• Udaipur District is bounded on the northwest by the Aravalli

Range.

14

Figure 3- Study area (Udaipur), Hyperion (FCC 47 28 15)

15

Geological Setting:

• The study area has two main stratigraphic units :

– Rocks of Aravali Supergroup (show a high degree of structural

complexity and deformation)

– Pre-Aravali Formations.

• Aravali Supergroup is divided into two groups- Delwara and

Debrai Groups.

• At some places the graywacke and phyllite rocks are not

deformed and display some typical sedimentary characters

like ripple marks, mud cracks, rain prints etc.

16

17 Figure 4-Geological map of Udaipur study area

Data Used & Methodology:

• The following data was used for the study:

– Hyperion Level 1R and Level 1Gst images

– Geological Map of the Study Area

– Spectral Library (USGS)

• Level 1R (L1R) - Radiometrically corrected only. No geometric

corrections are applied.

• Level 1Gst (L1Gst) - Radiometrically corrected and resampled for

geometric correction and registration to a geographic map

projection. The data image is ortho-corrected using (DEM) .

18

Hyperion L1Gst Data Hyperion L1R Data Geological Map

Preprocessing

Atmospheric Corrections using

FLAASH

Geometric Correction

MNF Transformation

Pixel Purity Index(PPI)

n-D visualizer

Spectral Library(USGS)

Resampling Spectral Analyst (Endmember

Identification)

Interpretation of

Geological units

Mapping (SAM,MTMF)

Mineralogical Mapping

Flow chart 1: The Flow Diagram of Methodology 19

Data Preprocessing:

• The Hyperion dataset has to be corrected for abnormal pixels,

striping prior to the atmospheric correction.

• Pre-processing is required not only for removing sensor errors

but also for display, band selection (to reduce the data

dimensionality) and to reduce computational complexity.

• A spatial subset was taken to focus on the study area

containing 198 bands(after removing bands containing errors

due to stripping)

20

Atmospheric corrections: (FLAASH)

• An algorithm called FLAASH ( Fast Line-of-sight Atmospheric

Analysis of Spectral Hypercubes) is used.

• FLAASH handles data from a variety of HSI and MSI sensors

and incorporates algorithms for water vapour and aerosol

retrieval and adjacency effect correction .

21

FLAASH input parameters

Sensor type

Pixel Size

Ground elevation

Scene centre Latitude/longitude

Sensor altitude

Visibility

Flight date and flight line

Atmospheric Model

Aerosol model

Water vapour retrieval

Spectral Polishing

Wavelength calibertion

Output reflectance scale parameter

22 Table 1: FLAASH Input Parametes

23

Fig 5: Analysis of Hyperspectral data

Observations:

• 144 bands were used for MNF trnsformation and the frist 8 eigen bands

containing most spectral information are used in PPI.

• PPI was calculated with 10000 iterations and a threshold factor of 2.5 for

extreme pixel selection.

• A total of 460 pixels were shortlisted and converted to Region of Interest.

• These pixel were plot into n-dimensional scatter plot to determine the

endmembers.

• The resampled USGS mineral library is used to identify the material of the

endmembers. The SAM and MTMF were used for the identification.

• Finally four minerals were identified through the process and they are

Grossularite, Pyrite, Calcite and Andradite.

24

Figure-6 Spectral profiles of Endmembers

25

Fig-7 Mineral abundance maps for Grossularite, Calcite.

26

27

Figure 8:

Case Study 2:

• Hyperspectral Image Analysis for Dolomite Identification in Tarbela

Dam Region of Pakistan.

• Author: Jibran Khan

• Journal:International Journal of Innovative Technology and

Exploring Engineering

• Objective: Indentification of dolomite using target idenfication

technique from EO-1 (Hyperion) satellite data.

• Study Area: Tarbela Dam on the Indus River in Pakistan is located in

Haripur District, Hazara Divisionabout 50 kilometres northwest of

Islamabad.

28

Fig 9 - Left: Red box showing Area of Interest (Image Source: USGS Earth explorer); Right:

Satellite Image of the Tarbela Dam on the Indus River of Pakistan (Source: NASA Astronaut

Photography Database

29

Geology of the Area:

• The notable minerals in Haripur district are sandstone,

limestone and dolomite.

• Hazara district Hills comprise crystalline and metamorphic

rocks with sedimentary deposits and gabbroic intrusions.

• The present geologic structure is the result of extensive

folding, shearing and faulting associated with regional crustal

deformation.

• The dolomite unit of Tarbela area consists of dark-weathering

interlayered brown and grey micro-crystalline dolomite. 30

EO-1 Hyperion:

• EO-1/Hyperion provides the highest available spectral resolution in

the field of satellite-borne remote sensing systems.

• Detailed classification of land assets through the Hyperion will

enable more accurate mineral exploration

Table 2: EO-1 Satellite Sensors Overview (Source: Satellite Imaging

Corporation, US) 31

Data processing and Analysis:

• Atmospheric correction is performed using the haze reduction

function of Erdas IMAGINE software (Intergraph Corporation).

• The de-hazing algorithm can turn a hazy data set into a crisp

and neat image.

• The second step in hyperspectral image processing is the

measurement of signal-to-noise ratio (SNR).

• In order to measure the SNR of haze-reduced Signal-to-Noise

function of Erdas IMAGINE software is used.

32

Fig 10: Left: Long narrow strip of EO-1 showing hyperspectral imagery of Tarbela

Dam region of Pakistan, Center: Haze reduced image, Right: In this image S/N

ratio model has been applied using Erdas IMAGINE

33

Contd…

• The next step involves the spectral profile analysis of imagery

with the spectral signature of dolomite.

• Erdas IMAGINE software contain spectral libraries (developed

by JPL,USGS) which contain spectral signature for a wide

variety of materials ranging from minerals, vegetation etc.

• Some specific points in the imagery were identified and their

spectral profiles are generted using the software.

• Then, this spectral profile was compared with the reference

spectral signature of dolomite available in spectral library.

34

Figure 11: Spectral profile of a selected point in the processed image

35

Figure 12: Image showing comparison of spectral profile of a selected

point in the processed image with the spectral signature of dolomite 36

Observations:

• The steps followed can be referred to as the preliminary steps

for the identification analysis of minerals.

• There was some uncertainty observe in the image processing

due to the presence of vegetation cover and noises.

• Some statistical tools such as statistical filtering and using bi-

variety regression analysis were suggested to get reliable

results.

37

Summary:

• Hyperspectral image analysis can be a very powerful tool for

cost effective analysis of minerals, identifying mineral

abundances and mapping the geological characteristics of an

area.

• Detection of minerals is dependent on the spectral coverage,

spectral resolution and signal to noise ratio of the

spectrometer, the abundance of the mineral.

• It can be said that the low signal to noise ratio and use of

laboratory spectra of the minerals from the standard spectral

libraries as the reference affect the classification results and

their accuracies. 38

References: • Khan.J.,(2013),Hyperspectral Image Analysis for Dolomite Identification in

Tarbela Dam Region of Pakistan, International Journal of Innovative

Technology and Exploring Engineering, Vol.2(3):pp 30-34

• Kruse, F.A.,(1998),Advances in Hyperspectral Remote Sensing for

Geological Mapping and Exploration, Proceedings 9th Australian Remote

Sensing Conference, Sydney, Australia, 23-24 July 1998.

• Sanjeevi.S.,(2008),Targeting Limestone and Bauxite deposits in Southern

India by spectral unmixing of hyperspectral image data, The International

A rchives of th Photogrammetry, Vol.XXXVII.PartB8.

• Singh.B, Dowerah.J.,(2010),Hyperspectral Imaging: New Generation

Remote Sensing, e-Journal Earth Science,Vol.3(3)

• Srivasthav. S.K, Prabhakaran.,(2012), Mineral Abundance Mapping Using

Hyperion Dataset in part of Udaipur, Rajasthan, 14th International

Conference on Geospatial Information Technology and Applications,

Gurgaon, India, 7-9 Feb 2012.

39