- The document discusses using hyperspectral remote sensing for mineral mapping. It provides background on how minerals have unique spectral signatures and defines hyperspectral imagery as image cubes with spatial and spectral data. - Two case studies are summarized that demonstrate using techniques like atmospheric correction, MNF transformation, and spectral analysis tools like SAM and MTMF on Hyperion satellite imagery to map minerals in areas of India and Pakistan. Key minerals identified include grossularite, calcite, pyrite, andradite, and dolomite. - The methodology involves preprocessing the hyperspectral cube, identifying endmembers, and then classifying and mapping minerals present based on their spectral properties and signatures in the imagery.

1. Hyperspectral Remote Sensing in
Mineral Mapping
Presented by
J S S Vani
1

2. Contents:
• Introduction
• Hyperspectral Image Analysis for Mineral Mapping
• Literature review
• Case Study 1
• Case Study 2
• Summary
• References
2

3. 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

4. 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

5. 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. 6
Fig 1: Concept of Hyperspectral imagery

7. • 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

8. • 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

9. • 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

10. • 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

11. 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

12. • 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

13. 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

14. 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

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

16. 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. 17Figure 4-Geological map of Udaipur study area

18. 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

19. 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

20. 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

21. 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

22. 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
22Table 1: FLAASH Input Parametes

23. 23
Fig 5: Analysis of Hyperspectral data

24. 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

25. Figure-6 Spectral profiles of Endmembers
25

26. Fig-7 Mineral abundance maps for Grossularite, Calcite.
26

27. 27
Figure 8:

28. 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

29. 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

30. 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

31. 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

32. 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

33. 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

34. 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

35. Figure 11: Spectral profile of a selected point in the processed image
35

36. Figure 12: Image showing comparison of spectral profile of a selected
point in the processed image with the spectral signature of dolomite
36

37. 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

38. 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

39. 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