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HYPERSPECTRAL RS IN MINERAL MAPPING

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  • 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