HYPERSPECTRAL DATA ANALYSIS FOR MINERAL MAPPING AROUND RAMPURA AGUCHA RAJASTHAN

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Hyperspectral Data Analysis for Mineral mapping, Bhilwara, Rajasthan

HYPERSPECTRAL DATA ANALYSIS FOR MINERAL MAPPING AROUND RAMPURA AGUCHA RAJASTHAN TEJ CHAVDA (PG191052) M. TECH GEOMATICS (2ND SEMESTER), FACULTY OF TECHNOLOGY, CEPT UNIVERSITY, AHMEDABAD, GUJARAT

For Mineral specific study this wavelength range are widely used.

1. ABSTRACT The Remote sensing techniques are helpful in the extraction of useful minerals. NASA’s Earth Observing – I (EOI) satellite was launched in Nov 2000 which plays very important role in spectral analysis for various applications such as agriculture, mineral mapping etc. Analysis of hyperspectral image spectra to identify mineralization zone and mineral spectral behavior.

Keywords: Mineral mapping; Spectral Angle Mapping: Hyperspectral Analysis,

2. INTRODUCTION Since the inception, remote sensing techniques playing valuable and significant contributions in the field of geology and mineral exploration. The combination of remote sensing and spectroscopy is known as imaging spectroscopy or hyperspectral remote sensing As part of the US New Millennium Program, the launch of the EO-1 satellite platform in November 2000 introduced hyperspectral sensing of the earth from space through its Hyperion system (Pearlman al., 1999 and Liao et al., 2000) albeit as a scientific experiment.

Hyperspectral sensors represent one of the most important technological trends in remote sensing, which combine imaging and spectroscopy in a single system. The system includes datasets composed of large (about 100 to 200) spectral bands of relatively narrow bandwidths (5-20 nm). Hyperspectral remote sensing, measuring hundreds of spectral bands from aircraft and satellite platforms, provides unique spatial/spectral

The current study is an attempt to identify and mapping of minerals in Rampuara Agucha Mines Bhilwara District Udaipur district falls in southern part of Rajasthan between latitudes 23°45'00" to 25°05'30" and longitudes 73°00’00” to 74°33'18". Earth Observer (EO) - 1 Hyperion Dataset is used for this study. Which was acquired on 20th Fab, 2012.

Spectral Angle Mapper (SAM) classification helps to detect and Mapped End-members such as Hematite, Montmorillonite and Pyrrhotite. From this study, it is evident that Hyperion EO-1 data can be effectively used for rapid mineral identification. For identification and abundance mapping for the mineral, in z-plot (Spectral plot), the wavelength range lies between 2.0-2.5 um.

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Hyperspectral Data Analysis for Mineral mapping, Bhilwara, Rajasthan

datasets for analysis of surface mineralogy (Goetz et al., 1985; Kruse et al., 2003). These data allow mapping of key iron mineralogy such as hematite as well as alteration minerals such as Hematite, Montmorolnite and Pyrohotatite (Clark et al., 1990).

Now suppose panchromatic which is a grey scale image. panchromatic band differentiate various feature just according to the grey scale suppose you taking AOI of water it will

Why hyperspectral imagery?

Hyperspectral imaging (HSI), or chemical imaging (CI), is the combination of spectroscopy and digital imaging. Most of the earth surface material have diagnostic absorption feature in the 400nm to 2500nm range the electromagnetic spectrum. And one of the important things in remote sensing is spectral reflectance curve so using that spectral reflectance curve we need to classify our hyperspectral data.

Most hyperspectral sensor measure hundreds of wavelength bands and most multispectral sensor measure only a few it’s not the number of measured wavelengths define a sensor as multispectral or hyperspectral it’s the narrowness and contiguous nature of the measurement even 20 band which are 10nm wide and contiguous in nature can also be called as hyperspectral sensor.

be having lower reflectance if it is urban feature building it will be having higher reflectance and if it’s vegetation than appear in between Now if we have more band like visible to NIR to SWIR channels in this particular wavelength if you farther sub categories that band than we can get that particular reflectance for the feature that spectral reflectance curve and you can identify that which kind of feature it is Multispectral data can do that but if we want to identify any material from the earth surface than we require a very batter details. Spectral resolution is higher in terms of narrow bands observation if I look at this observation from

Spectral resolution concept Spectral resolution is an ability of an instrument to discriminate lite into wavelength channels and using that we can differentiate the features. On the basis of spectral resolution your data set area categories in 4 type panchromatic multispectral hyperspectral and ultra-spectral 2

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Hyperspectral Data Analysis for Mineral mapping, Bhilwara, Rajasthan

hyperspectral sensor I can reconstruct my feature.

Broad band if we talk about only one band visible this is my panchromatic band the entire visible region moles in to one observation.

length. So, minerals can be uniquely identified. Two main causes for the absorption features include electric processes and vibrational processes. Hyperion uses two atmospheric windows, VNIR and SWIR, for mapping surface mineralogy because these wave-lengths are sensitive to a wide range of diagnostic EMRn material interactions.

Remote sensing has paramount importance not only for geological surveys of the developing countries but also for potential investors in the field of mineral exploration (Rokos et al., 2000). Thus, employing the advantages of remote sensing can help in reducing the degree of risk to investment in the mining sectors (Drury, 2001). Further understanding of ore deposit lenses and their pattern of mineral zonations (Cudahy et al., 2000) will facilitate much more mineral exploration activities.

Multispectral same visible region is broken down in to 3 broad spectral channel which is

3. AIM AND OBJECTIVE OF THIS STUDY The primary aims and objectives of the present work is to understand EO-1 Hyperion data processing and spectral analysis for mineral abundance mapping in part of Rampuara Agucha Mines Bhilwara District Udaipur district and to find greater application of hyperspectral remote sensing techniques in mineral exploration. This study attempts to map the various minerals present in the study area.

0.45-0.52, it can be depending on sensors it will be variable but more or less it is dividing into three regions.

And in hyperspectral visible region is divided into 100 spectral bands and later on ultraspectral bands one step ahead of hyperspectral is1000 of band width within the visible region if we take visible region only u get 1-3-100-1000 observation.

Hyperspectral Image contains the spectral contiguity, so we can get the each and every information of the object by specific band characteristics. It contains the 0.5-2.4 um EM spectrum Range.

4. OBJECTIVES The main objectives of the study are following: Validation of the Hyperion image spectra with the spectra generated from the field using Spectral Angle Mapper (SAM)

Different minerals have a unique reflectance and absorption pattern across different wave3

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Hyperspectral Data Analysis for Mineral mapping, Bhilwara, Rajasthan

5. STUDY AREA Rampuara Agucha Mines Bhilwara District Udaipur district falls in southern part of Rajasthan between latitudes 23°45'00" to 25°05'30" and longitudes 73°00’00” to 74°33'18".

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Rampur Agucha mines (Location 1-N 25°50`/ E74°44`)

Figure 2 Rajasthan District map, Highlighted Study area District

Figure 3 Study area Rajasthan, India Boundary

Figure 2 Hyperion data Full Resolution Browse in JPEG format (USGS)

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Hyperspectral Data Analysis for Mineral mapping, Bhilwara, Rajasthan

6. Data, Software I.

7. GEOLOGY OF THE AREA

Data Set used: I.

Hyperion Data

Rajasthan is endowed with a continuous geological sequence of rocks from the oldest Achaean, Metamorphites, represented by Bhilwara Super Group (more than 2,500 million years old) to sub-recent, alluvium and windblown sand. The western and northwestern parts of the state are covered by vast blanket of younger unconsolidated deposits including the blown sand of the Thar Desert of western Rajasthan. The remaining areas expose wide variety of hard rocks, which include varies types of metamorphic schist, quartzites, marble and gneisses of pre Cambrian age with associated acid, and basic intrusive rocks. The sediments included the rocks of Aravalli super group, Delhi Super group, Upper Precambrian Vindhyan Super group and of Cambrian of Jurassic, Cretaceous and tertiary ages. The south eastern extremely of the state is occupied by a pile of basaltic flows of Deccan Trap of Cretaceous age.

The datasets used for the project are hyperspectral image (2012 Feb, EO1 Hyperion Sensor). EO1 satellite data (Hyperion) has been used for the present study. Hyperion data covers the entire spectral range from 0.4 to 2.5 μm with 242 spectral bands at approximately 10 nm spectral resolution and 30m spatial resolution from a 705 km orbit. It is a push broom instrument with 242 spectral bands over a 7.5km wide swath along a 160 km path length. The system has two grating spectrometers – one in VNIR range (0.4 to 1.0 μm) and other in SWIR range (0.9 to 2.5 μm).

ENTITY ID

Date of acquisi tion

Path: Row

Latitu deLongit ude

EO1H148042201205111 0PZ_PF2_01

2012/0 2/20

148042

22.5/ 74.73

For the present study Hyperion data was downloaded from archive of USGS website in LIR (HDF). HDF format data was mainly used for processing. The raw HDF data is in

The Aravalli, one of the most ancient mountains in the world, have the oldest granitic and gneissic rocks at their base, overlain by the rocks of the Aravalli Super group, Delhi Super group, the Vindhyan Super group and younger rocks. These rocks are highly metamorphosed at certain place and show rich occurrence of minerals of great commercial important.

Band Interleaved by Line Format (BIL) format.

II.

Geology of Rajasthan

Software

The different type of software used include: ENVI 5.0, ArcGIS 10. ENVI was mainly used for processing and analyses of Hyperspectral image.

ArcGIS map preparation for study area.

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Hyperspectral Data Analysis for Mineral mapping, Bhilwara, Rajasthan

Figure 4 Geological Map Of India

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II.

Hyperspectral Data Analysis for Mineral mapping, Bhilwara, Rajasthan

Local Geology

Figure 5 Geological map of study area (Reference :

III.

Rampura-Agucha Mines Geological survey of India to put the Rampua-Agucha package of Graphitebearing-boitite-Sillimanite-garnet gneiss and calc-silicate rocks into an Agucha Group.

The study area a Rampura- Agucha deposit is located close (2km) to the Delwara Lineament (Gupta et al., 1980) between the Mangalwar Complex and the sandmata complex. The rocks of Rampura Agucha are Proterozoic metamorphosed sedimentary enclaves with an unknown stratigraphic contact with the Archean Basement (SinhaRoy. 1989). Recent thinking has led the

The regional geology of study area is very complex with intense migmatization and multiple phase of deformation. Despite extensive mapping, the high grade of metamorphism, extensive deformation and lack of outcrops hamper the understanding of

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Hyperspectral Data Analysis for Mineral mapping, Bhilwara, Rajasthan

the relationship between the various roc packages.

9. ANALYSIS AND INFFERANCE Atmospheric Correction of the datasets Atmospheric correction was applied to ensure the comparison of Hyperion data with reference spectra and to improve the quality of image.

The atmospheric correction module provides two atmospheric correction modelling tools for retrieving spectral reflectance from multispectral and hyperspectral radiance images: Quick Atmospheric correction (QUAC) and the Fast-Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH). With the Atmospheric correction module one can accurately compensate for atmospheric effects.

QUAC also corrects multispectral hyperspectral image data in the VNIR-SWIR range (λ= ̴0.4 - 2.5 μm) for atmospheric path radiance. It is based on the empirical finding that the average reflectance of a collection of diverse material spectra, such as the endmember spectra in a scene, is essentially scene-independent.

Figure 6 Geological map of Rampura-Agucha lead-zinc Mineral belt, Bhilwara

8. Methodology

Bad Bands Visualization When the reflected signals return back to the sensor some of the bands get affected by the noise and some bands might not have any return values which are called bad bands they can be removed by Band Animation or any other Atmospheric correction methods.

Figure 7 Methodology Flow Chart

Band No. 1 to 8 9 to 35 36 to 57 58 to 79 80 to 119 120 to 130 131 to 164 165 to 180 181 to 224 225 to 242

The methodology for the present study has been devised the primary objectives of the work. The methodology adopted for the present study is given above as a flow chart.

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Spectral range (nm) Status 356 - 436 No data 436 - 711 Good quality 711 - 935 Good quality 900 - 942 No data 942 - 1346 Good quality 1346 – 1457 No data 1457 – 1800 Good quality 1800 – 1961 No data 1961 - 2405 Good quality 2405 - 2577 No data

Remark Not considered Considered Considered

Not considered Considered

Not considered Considered

Not considered Considered

Not considered

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Hyperspectral Data Analysis for Mineral mapping, Bhilwara, Rajasthan

resources. The other approach is to use the data from all the spectral bands and map the useful information to a lower dimensional subspace.

3D Cube Generation for Bad Bands Visualization

Figure 9 Before Applying the correction

Principal component analysis (PCA) is the most commonly used method by remote sensing community for reducing the dimensionality of the data. PCA projects the data from original dimension to other orthogonal axis which has a lower dimension.

The outcome is a number of principal components in a descending order of information content.

Figure 9 After Applying the correction

Another dimensionality reduction process is the Minimum Noise Fraction (MNF) transformation, which is a modified form of PCA. Since there is high degree of correlation between adjacent spectral bands, the dimensionality of the dataset can be first reduced by applying the Minimum Noise Fraction (MNF) Transformation and retaining the small number of noise-free components. MNF transformation process is a noise adjusted principal components transform that estimates and equalizes the amount of noise in each image band to ensure that output components are ordered by their amount of information content.

In, Raw imagery bad bands removed After applying all the correction dimensionality of bands reduced 242 to 168. 40,31,13RGB

Figure 10 Before Atmospheric correction

Figure 11 correction

After

Atmospheric

Minimum Noise Fraction Transform The large volume of data generated during the Hyperspectral image processing makes handling and computation very complex.

Therefore data dimensionality reduction is necessary to improve analytical accuracy. In Hyperspectral analysis dimensionality reduction means band selection. Two approaches are possible for dimensionality reduction. The first approach is to select a small subset of the band which is sufficient to separate the various classes with required accuracy and amount of computational

MNF transformation was applied after atmospheric corrections. MNF results shows that band no 1 have highest eigenvalues (43.112623) and highest variance whereas values are decreasing with increasing band numbers. After analysing MNF plot and eigen values of bands initial 10 bands

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Hyperspectral Data Analysis for Mineral mapping, Bhilwara, Rajasthan

contains maximum variance therefore used for further processing .

Pixel Purity Index Automated process is applied to the MNF components to fix or locate the extreme spectra around the margins of the ndimensional data cloud. One such procedure is Pixel Purity Index (PPI). The Pixel Purity Index (PPI) is a means of finding the most “spectrally pure,� or extreme, pixels in multispectral and hyperspectral images (Boardman et al., 1995). The most spectrally pure pixels typically correspond to mixing endmembers. PPI is computed by repeatedly projecting n-dimensional (n-D) scatter plots onto a random unit vector. ENVI records the extreme pixels in each projection are recorded-those pixels that fall onto the ends of the unit vector and it notes the total number of times each pixel is marked as extreme. A PPI image is created where each pixel value corresponds to the number of times that pixel was recorded as extreme. The PPI is typically run on an MNF transform result; excluding the noise bands. PPI was calculated with 10000iterations and a threshold factor in the data unit was set to 1.5 for extreme pixel selection

Figure 12 Eigen Value plot

Figure 13 MNF FCC

Figure 15

Figure 16

Figure 17

Figure 14

BAND 1

BAND 2

BAND 3

BAND 4

The PPI was run on the MNF transform result excluding noise bands. The plot shows the total number of extreme pixels satisfying the threshold criterion found by the PPI processing as a function of the number of the iterations. It asymptotically approaches a flat line (Zero slope) when all of the extreme pixels are found. In the PPI image, brighter pixels represent more spectrally pure and extreme hits. Darker Pixels are less spectrally pure.

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Hyperspectral Data Analysis for Mineral mapping, Bhilwara, Rajasthan

The pixel purity index image was obtained with 10 MNF bands. The number of iterations was chosen as 10000 with 250 iterations per block, & a threshold of 1.5. The threshold was changed and tested on MNF image several times. The threshold value of 1.5 has given the best results and it has been chosen for PPI image generation.

Extraction of endmembers Selection of end-members can be obtained in three ways: 1. Selecting end-members from a spectral library using ROI – either already available spectral library like that of USGS, JPL 2. By generating spectral library from field or laboratory data.

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

3. From the image using Pixel Purity Index (PPI) and n- Dimensional visualization (n-D)

FEATURE IDENTIFICATION BY ROI’S Feature classes does get identified and classified by creating ROI’s (Region of Interests). Different feature classes have been identified from the in the dataset by using the crosshairs tool to select a single pixel and cross checking its spectral profile to an existing spectral profile of a material in the spectral library viewer. The closet match of spectral profile helps us identify the material of the object on ground. The identified feature classes are as follows:

Figure 18 PPI Plot

The PPI plot shows the total number of extreme pixels in 10000 iteration.

Feature class 1: Pyrrhotite Pyrrhotite is found in a wide range of hydrothermal deposits. It is often associated with mafic intrusive and volcanic rocks. It is found in massive sulfide deposits associated with pyrite, sphalerite, galena and chalcopyrite. Pyrrhotite is common at the Rampura Agucha Mines where it occurs with sphalerite, galena, chalcopyrite, silver and gold minerals.

:

Figure 19 PPI

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Hyperspectral Data Analysis for Mineral mapping, Bhilwara, Rajasthan

Figure 20 Spectral profile for Pyrrhotite

Figure 23 Spectral profile for Hematite

Figure 21 Spectral profile for Pyrrhotite

Figure 24 Spectral profile for Hematite The Spectral Profile for Hematite almost matches with the ground truth experiment profile. Overlapping of both the profile can be seen in the wavelength range 2030to 2380 nm

Feature class 3: Montmorillonite Montmorillonite is a very soft phyllosilicate mineral that typically forms in microscopic crystals, forming clay. The spectral characteristic of Montmorillonite is very much similar to that of Kaolinite.

Figure 22 Spectral profile for Pyrrhotite Each Feature exhibits a unique Spectral signature across the electromagnetic spectrum here the ROI taken for the pyrrhotite matches with the spectral library at the wavelength of 2150 to 2380 nm

Feature class 2 : Hematite Hematite is an oxide of iron having composition Fe2O3. Pure hematite, also called “red ore” (in fact, the name hematite comes from the word “hemo” meaning blood), contains 70% of Fe. Hematite, also spelled as Haematite, is the mineral form of iron oxide (Fe2O3). Hematite is a very common mineral, colored black to steel or silver-gray, brown to reddish brown, or red.

Figure 25 Spectral profile for Montmorillonite The Spectral Profile for pyrrhotite almost matches with the ground truth experiment profile. Overlapping of both the profile can be seen in the wavelength range 1800 to 2300 nm

Spectral Angle Mapper (SAM): SAM is an automated classification method for comparing image spectra to known spectra. The spectral angle mapper or SAM is a physically based spectral angle mapper.

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Hyperspectral Data Analysis for Mineral mapping, Bhilwara, Rajasthan

End members are the purest pixels of a feature class. The end member here has been selected on the bases of Roi’s taken earlier in the study. Pixel purity index (PPI) is also a method that can be used in SAM. In SAM the radiance angle is compared to each pixel.

Smaller the angle lower is the scope for error.

SAM classification uses reflectance data.

Reference spectra, or training classes, can come from any of the following sources:

Rampuara Agucha Mines Pyrrhotite

➢ Spectral libraries ➢ ASCII files ➢ Regions of interest (ROIs)

Hematite Montmorillonite

For the SAM classification to be effective, the input image must be converted to apparent reflectance so that the data units are the same as the library units.

Maximum angle for SAM Classification is taken as 0.10 in order to classify it more accurately.

Class Pyrrhotite Hematite Montmorillonite

Pixel count 4130 93628 35944

Percentage% 0.9266 21.0071 8.06469

Rule image: Each rule image represents the spectral angle between the image spectrum and one reference spectrum. Darker pixels in the rule images represent smaller spectral angles and thus image spectra that are more similar to the reference spectrum.

After all the ROI’s were identified, SAM classification was performed on the QUAC image.

Classification image: This image assigns each pixel to a class that represents one of the materials from the selected library spectrum. The classification chooses the library spectrum that has the smallest spectral angle with each pixel in the input image. So, the classification image shows the best match to a given material for each pixel.

Pyrrhotite Figure 26 Pyrrhotite SAM Rule Imagery

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Hyperspectral Data Analysis for Mineral mapping, Bhilwara, Rajasthan

Here confusion matrix was created using second method that is confusion matrix using ground truth ROIs. The columns represent the true classes, while row represents the classifier’s prediction. The matrix is square, with all correct classifications along the upper-left to lower-right diagonal. The confusion matrix is based on no. of pixels. The overall accuracy is calculated by summing up the correctly classified values and dividing it by no. of total values. Kappa coefficient measures the agreement between classification and truth. A kappa value of 1 represent perfect agreement (which is impossible to achieve), while 0 represents no agreement.

Hematite

Figure 27 Montmorillonite SAM Rule Imagery

Overall accuracy of 68.2997% was obtained with kappa coefficient being 0.5366.

CONCLUSION

Montmorillonite

Figure 28 Hematite SAM Rule Imagery

ACCURACY ASSESSMENT Accuracy of classification can be assessed using confusion matrix and kappa statistics. It can be done by either of the following two ways: 1) Confusion Matrix Using Ground Truth Image 2) Confusion Matrix Using Ground Truth ROIs

Figure 29 Confusion Matrix

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The study illustrates that Hyperion data is useful for identifying mineral abundances and mapping the geological characteristics.

Minerals identified are in accordance with the ground lithology, but the spatial distribution of those minerals in the map is affected by topographic disturbances and presence of vegetation. In conclusion, it can be said that the low signal to noise ratio of Hyperion and presence of small effect in the image and the use of laboratory spectra of these minerals from the standard spectral libraries as the reference affected the classification results and their accuracies.

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

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Hyperspectral Data Analysis for Mineral mapping, Bhilwara, Rajasthan

10. REFERENCES ➢ ➢ ➢ ➢ ➢ ➢ ➢ ➢ ➢ ➢

➢ ➢

Vedas by SAC, ISRO Richa Upadhyay and D.R S. K Srivastav,” Mineral abundance mapping in Udaipur using Hyperion data”, IIRS, Dehradun EO-1/ Hyperion Science Data User’s Guide, Level 1_B, NASA, 11-12.

Bhattacharya, “Mapping of altered/clay Minerals over parts of Dongargarh, Chattisgarh Using EO-1 Hyperion data”, IJRS, 2012 Classification accuracy & Kappa coefficient, ENVI Tutorial Spectral Angle Mapper Classification (SAM) , ENVI Tutorial http://www.geologydata.info/udaipur_geolo gy.htm https://www.researchgate.net/publication/33 8377275_Optimized_Lithological_Mapping _from_Multispectral_and_Hyperspectral_R emote_Sensing_Images_Using_Fused_Multi -Classifiers https://www.slideshare.net/gokulanand36/di stribution-of-lead-in-india www.geolmlsu.org

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