Mineral Abundance Mapping Using Hyperion Dataset in Part of Udaipur Dist.

Rajasthan, India

Presented By

Salaj.S.SPSNA College of Engineering and Technology

Dindigul, TamilNadu

IntroductionIntroduction• Hyperspectral remote sensing , measuring hundreds

of spectral bands from aircraft and satellite platforms, provides unique spatial/spectral datasets for analysis of surface mineralogy (Goetz et al., 1985; Kruse et al.,2003). Their use for geologic applications is well established (Goetz et al., 1985; Kruse et al., 1999,2003; Rowan and Mars, 2003).

• For the present study Hyperion, a hyperspectral imager on-board of EO-1 satellite is used contains 220 spectral bands ranging from 400-2500nm and the spatial resolution is 30 meter per pixel, swath width is 7.5 km by 42 or 100 km area.

Comparison between Multispectral and Hyperspectral Comparison between Multispectral and Hyperspectral

sensorssensors

Study AreaStudy Area

Hyperion data (FCC 40 31 13)

• Part of Udaipur district – Aravalli Fold Belt, Rajasthan covering about 303.43 sq. km

• Extent: 73˚ 33’ 25” E to 73˚42’53”

E 24˚ 09’ 34” N to 24˚ 31’ 40” N

• The average height of the area is 598m from the sea level

Aims and ObjectivesAims and Objectives

The main aim of the study is to test the abilities of Hyperion data for mineral abundance mapping in the study area. The broad objectives are as follows

•Atmospheric Correction of Hyperion Data.

•Extraction of Endmembers from the image using advanced techniques like MNF, PPI etc.

•Mineralogical mapping using the various endmembers extracted from the image.

Data UsedData Used• Hyperion image (Path : 148, Row : 43)

L1R Image - Radiometrically corrected

L1Gst – Radiometrically as well as geometrically and Topographically corrected

• Spectral Library(USGS)

• Geological Map of the study area

Software Used

• ENVI 4.7

• Arc GIS 9.3

MethodologyMethodology

Hyperion L1R Data

Pre processing

Atmospheric correction using FLAASH

Geometric Correction

Hyperion L1Gst Data

MNF Transformation

Pixel Purity Index (PPI)

n-D visualizer

Spectral Analyst (Endmember Identification)

Spectral Library(USGS)

Resampling

Mapping Methods

SAM MTMF

Mineralogical Mapping

Geological Map

Interpretation of Geological Units

PreprocessingPreprocessing

Bad Column RemovalEach bad column was replaced by average of previous and next column

Before correction After correction

Atmospheric Correction using FLAASH ModuleAtmospheric Correction using FLAASH Module

Atmospheric Correction using FLAASH ModuleAtmospheric Correction using FLAASH ModuleSensor type Hyperion

Pixel size 30

Ground elevation .6 km

Scene Centre Lat/Long 24.6◦N,73.7◦E

Visibility 40 km

Sensor altitude 705km

Flight date & Flight time 19/01/2004,5:22:17

Atmospheric model Tropical

Aerosol model Rural

Water vapour retrieval 1135nm

Wavelength Calibration Yes

Advanced parameters

Output reflectance Scale factor

10000

MODTRAN resolution 15 cms-1

MODTRAN multi scattering model

Scaled DISORT 8 Stream

CO2 390 ppm

Atmospheric CorrectionAtmospheric Correction

Geometric CorrectionGeometric Correction

Hyperion L1Gst image as base image and L1R image as warp image(Auto-image-to-image registration)

Dimensionality ReductionDimensionality Reduction

• Minimum Noise fraction was used to reduce spectral dimensionality and redundancy

• Noise estimation from image

• Separating noise from image

• New uncorrelated 8 bands from 144original bands

Comparison of Geological map with MNF color compositesComparison of Geological map with MNF color composites

MNF(R:2 G:3 B:6)

Shelf Facies – Delwara Group and Debari Group

Deep Sea Facies - Jarol Group and Lunavada Group with Ultramafics

Pixel Purity Index(PPI)Pixel Purity Index(PPI)

N-Dimensional VisualizerN-Dimensional Visualizer• selecting the endmembers in n-D space.

• Pure pixels can be viewed in any angle by rotating the n-D scatterplot

• The clusters can be saved as ascii files

• Finalization of clusters after using spectral analyst

Endmember Identification Using Spectral AnalystEndmember Identification Using Spectral Analyst

• Endmember identification using Spectral Angle Mapper and Spectral Feature Fitting with equal weightage

• Mineral with highest score was identified as material for a particular cluster

Mapping Methods

• SAM (Spectral Angle Mapper)

• Mixture Tuned Matched Filtering(MTMF)

Post ClassificationPost Classification

End Member Collection Spectra

• Rule Classifier

Mineral Abundance MapsMineral Abundance Maps1) Grossularite (Ca3Al2Si3O12) -

Grossularite is especially characteristic of both thermally and regionally metamorphosed impure calcareous rocks. It also occurs in the rocks which have undergone calcium metamorphism. It may result from replacement of Wollastonite. It also occurs in association with serpentinite and has been described from highly metamorphosed layered complex possibly resulting from the alteration of anorthite.

2) Calcite (CaCO3) –

Calcite is one of the most ubiquitous minerals, and in addition to being an important rockforming mineral in sedimentary environments, it also occurs in metamorphic and igneous rocks and is a common mineral of hydrothermal and secondary mineralization. In sedimentary rocks Calcite is the principal constituent of most limestones. It occurs both as a primary precipitate and in the form of fossil shells. Calcite is the stable form of CaCO3 and although approximately equal numbers of organisms make their shells of Calcite and aragonite (or, as for some of the mollusca, of both), the aragonite eventually undergoes recrystallization to calcite.

3) Pyrite (FeS2) -

Pyrite is similar in appearance to chalcopyrite, pyrrhotite and marcasite, It can be distinguished from chalcopyrite since the latter mineral has a deeper yellow colour in reflected light, and is softer, being scratched by a knife, Pyrrhotite is bronze rather than brasscoloured, is also scratched by a knife and usually magnetic.

4) Andradite (Ca3(Fe+3, Ti)2Si3O12) -

Typically occurs in contact or thermally metamorphosed impure calcareous sediments and particularly in the metamorphism and particularly metasomatic skarn deposits. This involves the addition of Fe2O2 and SiO2. If FeO is also introduced, hedenbergite may form in addition to andradite, and if insufficient silica is available magnetite may result giving the typical andradite-hedenbergite-magnetite skarn assemblage. Andradite also occurs as the result of metasomatism connected with the thermal metamorphism of calcic igneous rocks such as andesite

Mineral MapMineral Map

ConclusionConclusion

• Mineralogical mapping can be achieved using Hyperion data.

• Mapping is affected by topography and vegetation and ground in-situ conditions.

• False positives may be generated

• Limitations due to Standard spectral library.

• Need of Field spectral library for minerals is essential for accurate mapping.

ReferencesReferences• Kruse, F.A., J.W. Boardman And J.F. Huntington (2003): Evaluation and

validation of hyperion for mineral mapping, IEEE Trans. Geosci. Remote Sensing, 41 (6), 1388-1400.

• Kruse, F.A., J.W. Boardman And J.F. Huntington (1999), Fifteen years of hyperspectral data: Northern Grapevine Mountains, Nevada, in Proceedings of the 8th JPL Airborne Earth Science Workshop, Jet Propulsion Laboratory Publ. 99-17, 247-258.

• Rowan, L.C. And J.C. Mars (2003): Lithologic mapping in the Mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection Spectrometer (ASTER) data, Remote Sensing Environ., 84,350-366.

• S Sinha-Roy, G Malhotra, M Mohanty (1998), Geology of Rajasthan, Geological Society of India, 1st edition, 278 p.

• Deer W. A., Howie R. A. and Zussman J, 1966, Mineralogy, Longman Group Limited, 7th edition, 528p.

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