Automated lithological mapping by integrating spectral enhancement techniques and machine learning algorithms using AVIRIS-NG hyperspectral data in Gold-bearing granite-greenstone rocks in Hutti, India

Abstract In this study, we proposed an automated lithological mapping approach by using spectral enhancement techniques and Machine Learning Algorithms (MLAs) using Airborne Visible Infrared Imaging Spectroradiometer-Next Generation (AVIRIS-NG) hyperspectral data in the greenstone belt of the Hutti area, India. We integrated spectral enhancement techniques such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformation and different MLAs for an accurate mapping of rock types. A conjugate utilization of conventional geological map and spectral enhancement products derived from ASTER data were used for the preparation of a high-resolution reference lithology map. Feature selection and extraction methods were applied on the AVIRIS-NG data to derive different input dataset such as (a) all spectral bands, (b) shortwave infrared bands, (c) Joint Mutual Information Maximization (JMIM) based optimum bands, and (d) optimum bands using PCA, to choose optimum input dataset for automated lithological mapping. The comparative analysis of different MLAs shows that the Support Vector Machine (SVM) outperforms other Machine Learning (ML) models. The SVM achieved an Overall Accuracy (OA) and Kappa Coefficient (k) of 85.48% and 0.83, respectively, using JMIM based optimum bands. The JMIM based optimum bands were more suitable than other input datasets to classify most of the lithological units (i.e. metabasalt, amphibolite, granite, acidic intrusive and migmatite) within the study area . The sensitivity analysis performed in this study illustrates that the SVM is less sensitive to the number of samples and mislabeling in the model training than other MLAs. The obtained high-resolution classified map with accurate litho-contacts of amphibolite, metabasalt, and granite can be coupled with an alteration map of the area for targeting the potential zone of gold mineralization.

[1]  A. Ghulam,et al.  Lithological mapping in the Central Eastern Desert of Egypt using ASTER data , 2010 .

[2]  Stephen Marshall,et al.  Effective Feature Extraction and Data Reduction in Remote Sensing Using Hyperspectral Imaging [Applications Corner] , 2014, IEEE Signal Processing Magazine.

[3]  K. Vinod Kumar,et al.  Spectroscopic study of rocks of Hutti-Maski schist belt, Karnataka , 2012, Journal of the Geological Society of India.

[4]  Y. Ninomiya,et al.  Detecting lithology with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral thermal infrared “radiance-at-sensor” data , 2005 .

[5]  Matthew J. Cracknell,et al.  Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information , 2014, Comput. Geosci..

[6]  Hongwei Xie,et al.  Classification of Solder Joint Using Feature Selection Based on Bayes and Support Vector Machine , 2013, IEEE Transactions on Components, Packaging and Manufacturing Technology.

[7]  B. Mishra,et al.  Alteration geochemistry and fluid inclusion characteristics of the greenstone-hosted gold deposit of Hutti, Eastern Dharwar Craton, India , 2002, Mineralium Deposita.

[8]  E. Bedini.  Mapping lithology of the Sarfartoq carbonatite complex, southern West Greenland, using HyMap imaging spectrometer data , 2009 .

[9]  W. Calvin,et al.  Surface mineral mapping at Steamboat Springs, Nevada, USA, with multi-wavelength thermal infrared images , 2005 .

[10]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[11]  L. Rowan,et al.  Lithologic mapping of the Mordor, NT, Australia ultramafic complex by using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) , 2005 .

[12]  F. Fueten,et al.  Mineral identification using artificial neural networks and the rotating polarizer stage , 2001 .

[13]  Snehamoy Chatterjee,et al.  Potential of Airborne Hyperspectral Data for Geo-Exploration over Parts of Different Geological/Metallogenic Provinces in India based on AVIRIS-NG Observations , 2019, Current Science.

[14]  F. Meer.  Analysis of spectral absorption features in hyperspectral imagery , 2004 .

[15]  Fred A. Kruse,et al.  The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data , 1993 .

[16]  A. B. Sarbadhikari,et al.  Fluid inclusion characteristics of the Uti gold deposit, Hutti-Maski greenstone belt, southern India , 2005 .

[17]  Amir Hossein Alavi,et al.  Machine learning in geosciences and remote sensing , 2016 .

[18]  A. Thorpe.  Mapping methane concentrations from a controlled release experiment using the next generation Airborne Visible/Infrared Imaging Spectrometer (AVIRISng) , 2014 .

[19]  Lawrence D. Jackel,et al.  Limits on Learning Machine Accuracy Imposed by Data Quality , 1995, KDD.

[20]  Tsehaie Woldai,et al.  Multi- and hyperspectral geologic remote sensing: A review , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[21]  R. Clark,et al.  Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications , 1984 .

[22]  Ana Cláudia Teodoro,et al.  Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco , 2019, ISPRS Int. J. Geo Inf..

[23]  Xiya Zhang,et al.  Lithological mapping from hyperspectral data by improved use of spectral angle mapper , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[24]  Steve R. Gunn,et al.  Result Analysis of the NIPS 2003 Feature Selection Challenge , 2004, NIPS.

[25]  Thomas Oommen,et al.  Sampling Bias and Class Imbalance in Maximum-likelihood Logistic Regression , 2011 .

[26]  A. Crósta,et al.  Targeting key alteration minerals in epithermal deposits in Patagonia, Argentina, using ASTER imagery and principal component analysis , 2003 .

[27]  Emmanuel John M. Carranza,et al.  Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines) , 2015, Comput. Geosci..

[28]  Aboul Ella Hassanien,et al.  Linear discriminant analysis: A detailed tutorial , 2017, AI Commun..

[29]  B. Mishra,et al.  Diverse Tourmaline Compositions from Orogenic Gold Deposits in the Hutti-Maski Greenstone Belt, India: Implications for Sources of Ore-Forming Fluids , 2015 .

[30]  F. Kruse.  Identification and mapping of minerals in drill core using hyperspectral image analysis of infrared reflectance spectra , 1996 .

[31]  G. Hunt.  SPECTRAL SIGNATURES OF PARTICULATE MINERALS IN THE VISIBLE AND NEAR INFRARED , 1977 .

[32]  Philippa J. Mason,et al.  Hyperspectral remote sensing for mineral exploration in Pulang, Yunnan Province, China , 2011 .

[33]  Christophe Delacourt,et al.  Using ASTER remote sensing data set for geological mapping, in Namibia , 2005 .

[34]  Qiuming Cheng,et al.  Lithological Classification Using Sentinel-2A Data in the Shibanjing Ophiolite Complex in Inner Mongolia, China , 2018, Remote. Sens..

[35]  Jonathan Cheung-Wai Chan,et al.  Multiple Criteria for Evaluating Machine Learning Algorithms for Land Cover Classification from Satellite Data , 2000 .

[36]  Nurdan Akhan Baykan,et al.  Mineral identification using color spaces and artificial neural networks , 2010, Comput. Geosci..

[37]  Alexander F. H. Goetz,et al.  Three decades of hyperspectral remote sensing of the Earth: a personal view. , 2009 .

[38]  Yongliang Chen,et al.  Mapping mineral prospectivity using an extreme learning machine regression , 2017 .

[39]  Huan Liu,et al.  Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution , 2003, ICML.

[40]  Lloyd A. Smith,et al.  Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper , 1999, FLAIRS.

[41]  Yoshua Bengio,et al.  Algorithms for Hyper-Parameter Optimization , 2011, NIPS.

[42]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[43]  A. Shetty,et al.  Lithological Discrimination and Mapping using ASTER SWIR Data in the Udaipur area of Rajasthan, India☆ , 2015 .

[44]  Le Yu,et al.  Towards automatic lithological classification from remote sensing data using support vector machines , 2010, Comput. Geosci..

[45]  L. Rowan,et al.  Lithologic mapping in the Mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data , 2003 .

[46]  T. Cudahy,et al.  Seamless geological map generation using ASTER in the Broken Hill-Curnamona province of Australia , 2005 .

[47]  Richard Gloaguen,et al.  Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq) , 2014, Remote. Sens..

[48]  S. Sanjeevi,et al.  Jeffries Matusita based mixed-measure for improved spectral matching in hyperspectral image analysis , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[49]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[50]  Richard Gloaguen,et al.  Integration of spectral, spatial and morphometric data into lithological mapping: A comparison of different Machine Learning Algorithms in the Kurdistan Region, NE Iraq , 2017 .

[51]  P. V. Raju,et al.  Reflectance spectroscopy and ASTER based mapping of rock-phosphate in parts of Paleoproterozoic sequences of Aravalli group of rocks, Rajasthan, India , 2018, Ore Geology Reviews.

[52]  Robert O. Green,et al.  An Overview of AVIRIS-NG Airborne Hyperspectral Science Campaign Over India , 2019, Current Science.

[53]  M. Hashim,et al.  Mapping alteration mineral zones and lithological units in Antarctic regions using spectral bands of ASTER remote sensing data , 2018 .

[54]  James V. Taranik,et al.  Hydrothermal Alteration Mapping at Bodie, California, Using AVIRIS Hyperspectral Data , 1998 .

[55]  T. Kusky,et al.  ASTER spectral ratioing for lithological mapping in the Arabian–Nubian shield, the Neoproterozoic Wadi Kid area, Sinai, Egypt , 2007 .

[56]  Amba Shetty,et al.  Sub-pixel mineral mapping using EO-1 Hyperion hyperspectral data , 2014 .

[57]  F. Sabins,et al.  Remote sensing for mineral exploration , 1999 .

[58]  Timothy A. Warner,et al.  Implementation of machine-learning classification in remote sensing: an applied review , 2018 .

[59]  Sildomar T. Monteiro,et al.  Evaluating Classification Techniques for Mapping Vertical Geology Using Field-Based Hyperspectral Sensors , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[60]  Abbas Bahroudi,et al.  Support vector machine for multi-classification of mineral prospectivity areas , 2012, Comput. Geosci..

[61]  E. Bedini.  Mineral mapping in the Kap Simpson complex, central East Greenland, using HyMap and ASTER remote sensing data , 2011 .

[62]  Mazlan Hashim,et al.  ASTER, ALI and Hyperion sensors data for lithological mapping and ore minerals exploration , 2014, SpringerPlus.

[63]  Mahesh Pal,et al.  Random forest classifier for remote sensing classification , 2005 .

[64]  R. Zuo.  Machine Learning of Mineralization-Related Geochemical Anomalies: A Review of Potential Methods , 2017, Natural Resources Research.

[65]  Rick Archibald,et al.  Feature Selection and Classification of Hyperspectral Images With Support Vector Machines , 2007, IEEE Geoscience and Remote Sensing Letters.

[66]  T. Oommen,et al.  Mapping hydrothermal alteration minerals using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski gold deposit area, India , 2019, International Journal of Remote Sensing.

[67]  Fred A. Kruse,et al.  Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping , 2003, IEEE Trans. Geosci. Remote. Sens..

[68]  S. Shanmugam,et al.  Spectral matching approaches in hyperspectral image processing , 2014 .

[69]  A. Guha,et al.  Potential Utility of Spectral Angle Mapper and Spectral Information Divergence Methods for mapping lower Vindhyan Rocks and Their Accuracy Assessment with Respect to Conventional Lithological Map in Jharkhand, India , 2018, Journal of the Indian Society of Remote Sensing.

[70]  Jilei Liu,et al.  Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data , 2016 .

[71]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[72]  Gustavo E. A. P. A. Batista,et al.  A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.

[73]  D. Groves,et al.  Timing of gold mineralization in the Hutti gold deposit, Dharwar Craton, South India , 2008 .

[74]  Rossitza Setchi,et al.  Feature selection using Joint Mutual Information Maximisation , 2015, Expert Syst. Appl..

[75]  E. Cloutis,et al.  Review Article Hyperspectral geological remote sensing: evaluation of analytical techniques , 1996 .

[76]  V. Rodriguez-Galiano,et al.  Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines , 2015 .

[77]  Giles M. Foody,et al.  Feature Selection for Classification of Hyperspectral Data by SVM , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[78]  Gang Luo,et al.  A review of automatic selection methods for machine learning algorithms and hyper-parameter values , 2016, Network Modeling Analysis in Health Informatics and Bioinformatics.

[79]  J. R. Sveinsson,et al.  Mapping of hyperspectral AVIRIS data using machine-learning algorithms , 2009 .

[80]  T. Warner,et al.  Integrating visible, near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite, Nevada: a rule-based system , 2010 .

[81]  Dale J. Prediger,et al.  Coefficient Kappa: Some Uses, Misuses, and Alternatives , 1981 .

[82]  Carlos Roberto de Souza Filho,et al.  A review on spectral processing methods for geological remote sensing , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[83]  F. Kruse,et al.  District-level mineral survey using airborne hyperspectral data, Los Menucos, Argentina , 2006 .

[84]  Jugal K. Kalita,et al.  MIFS-ND: A mutual information-based feature selection method , 2014, Expert Syst. Appl..

[85]  Jon Atli Benediktsson,et al.  Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[86]  E Mjolsness,et al.  Machine learning for science: state of the art and future prospects. , 2001, Science.