Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data

Texture as a measure of spatial features has been useful as supplementary information to improve image classification in many areas of research fields. This study focuses on assessing the ability of different textural vectors and their combinations to aid spectral features in the classification of silicate rocks. Texture images were calculated from Landsat 8 imagery using a fractal dimension method. Different combinations of texture images, fused with all seven spectral bands, were examined using the Jeffries–Matusita (J–M) distance to select the optimal input feature vectors for image classification. Then, a support vector machine (SVM) fusing textural and spectral features was applied for image classification. The results showed that the fused SVM classifier achieved an overall classification accuracy of 83.73%. Compared to the conventional classification method, which is based only on spectral features, the accuracy achieved by the fused SVM classifier is noticeably improved, especially for granite and quartzose rock, which shows an increase of 38.84% and 7.03%, respectively. We conclude that the integration of textural and spectral features is promising for lithological classification when an appropriate method is selected to derive texture images and an effective technique is applied to select the optimal feature vectors for image classification.

[1]  I. Good,et al.  Fractals: Form, Chance and Dimension , 1978 .

[2]  J. Dubois,et al.  Evaluation Of The Grey-level Co-occurrence Matrix Method For Land-cover Classification Using Spot Imagery , 1990 .

[3]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[4]  V. Radeloff,et al.  Image texture as a remotely sensed measure of vegetation structure , 2012 .

[5]  Mario Chica-Olmo,et al.  Computing geostatistical image texture for remotely sensed data classification , 2000 .

[6]  Anne B. Kahle,et al.  Mapping of hydrothermal alteration in the Cuprite mining district, Nevada, using aircraft scanner images for the spectral region 0.46 to 2.36µm , 1977 .

[7]  B. Zoheir,et al.  Integrating geologic and satellite imagery data for high-resolution mapping and gold exploration targets in the South Eastern Desert, Egypt , 2012 .

[8]  Guoan Bi,et al.  On Texture Classification Using Fractal Dimension , 1999, Int. J. Pattern Recognit. Artif. Intell..

[9]  B. Leblon,et al.  Rock unit discrimination on Landsat TM, SIR-C and Radarsat images using spectral and textural information , 2004 .

[10]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  P. S. Kealy,et al.  A comparison of techniques for extracting emissivity information from thermal infrared data for geologic studies , 1992 .

[12]  M. Goodchild.  Fractals and the accuracy of geographical measures , 1980 .

[13]  C. Woodcock,et al.  Combining Spectral and Texture Data in the Segmentation of Remotely Sensed Images , 1996 .

[14]  Mazlan Hashim,et al.  Detection of chromite bearing mineralized zones in Abdasht ophiolite complex using ASTER and ETM+ remote sensing data , 2013, Arabian Journal of Geosciences.

[15]  Sandeep Kumar,et al.  Appl. Sci , 2013 .

[16]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[17]  K. Tansey,et al.  Lithological mapping of the Troodos ophiolite, Cyprus, using airborne LiDAR topographic data , 2010 .

[18]  Björn Waske,et al.  Classifying Multilevel Imagery From SAR and Optical Sensors by Decision Fusion , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Hua Su,et al.  Classification of MODIS images combining surface temperature and texture features using the Support Vector Machine method for estimation of the extent of sea ice in the frozen Bohai Bay, China , 2015 .

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

[21]  Mehmet Fatih Akay,et al.  Support vector machines combined with feature selection for breast cancer diagnosis , 2009, Expert Syst. Appl..

[22]  Martha C. Anderson,et al.  Landsat-8: Science and Product Vision for Terrestrial Global Change Research , 2014 .

[23]  T. Warner,et al.  Integrating visible, near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite, Nevada , 2007 .

[24]  F. Girosi,et al.  Nonlinear prediction of chaotic time series using support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[25]  Yoshiki Ninomiya,et al.  Quartz Index, Carbonate Index and SiO2 Content Index Defined for ASTER TIR Data , 2002 .

[26]  Liming Liu,et al.  A method of salt-affected soil information extraction based on a support vector machine with texture features , 2010, Math. Comput. Model..

[27]  M. Qari.  Application of landsat TM data to geological studies, Al-Khabt area, southern Arabian shield , 1991 .

[28]  V. Robinson,et al.  Texture augmented detection of macrophyte species using decision trees , 2013 .

[29]  Ahmad Taher Azar,et al.  Performance analysis of support vector machines classifiers in breast cancer mammography recognition , 2013, Neural Computing and Applications.

[30]  Shuhab D. Khan,et al.  Lithological mapping of Bela ophiolite with remote-sensing data , 2011 .

[31]  C. DaCamara,et al.  Land surface temperature and emissivity estimation based on the two-temperature method: sensitivity analysis using simulated MSG/SEVIRI data , 2004 .

[32]  Giampiero Naletto,et al.  Mapping the Buraburi granite in the Himalaya of Western Nepal: Remote sensing analysis in a collisional belt with vegetation cover and extreme variation of topography , 2011 .

[33]  A. Collins.  Thermal infrared spectra and images of altered volcanic rocks in the Virginia Range, Nevada , 1991 .

[34]  O. Mutanga,et al.  Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands using field spectrometry , 2009 .

[35]  J. Kittler.  Image processing for remote sensing , 1983, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[36]  P. Hauff,et al.  Alteration Mapping in Exploration: Application of Short-Wave Infrared (SWIR) Spectroscopy , 1999, SEG Discovery.

[37]  Peijun Li,et al.  Lithological discrimination of Altun area in northwest China using Landsat TM data and geostatistical textural information , 2001 .

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

[39]  Melvin B. Satterwhite,et al.  Effects of lichens on the reflectance spectra of granitic rock surfaces , 1985 .

[40]  K. Chou,et al.  Support vector machines for predicting membrane protein types by using functional domain composition. , 2003, Biophysical journal.

[41]  E. Ben-Dor.  The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400-2500 nm) during a controlled decomposition process , 1997 .

[42]  S. Franklin.  Using spatial Co-occurrence texture to increase forest structure and species composition classification accuracy , 2001 .

[43]  W. Pan,et al.  A Study of Remote Sensing Image Landform Frame and Lithologic Component Decomposing Algorithm and Multifractal Feature of Rock Types , 2009 .

[44]  S. Nalbant,et al.  The use of Landsat Thematic Mapper imagery for analysing lithology and structure of Korucu-Du[ggrave] la area in western Turkey , 1995 .

[45]  Hui Fan,et al.  Land-cover mapping in the Nujiang Grand Canyon: integrating spectral, textural, and topographic data in a random forest classifier , 2013 .

[46]  S. Myint,et al.  Fractal approaches in texture analysis and classification of remotely sensed data: Comparisons with spatial autocorrelation techniques and simple descriptive statistics , 2003 .

[47]  Vladimir Vapnik.  The Vicinal Risk Minimization Principle and the SVMs , 2000 .

[48]  Michael Sawada,et al.  A comparison of classification algorithms using Landsat-7 and Landsat-8 data for mapping lithology in Canada’s Arctic , 2015 .

[49]  H. Ranjbar,et al.  Application of Principal Component Analysis and Spectral Angle Mapper in the Mapping of Hydrothermal Alteration in the Jebal–Barez Area, Southeastern Iran , 2012 .

[50]  Craig A. Coburn,et al.  A multiscale texture analysis procedure for improved forest stand classification , 2004 .

[51]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[52]  Bernhard Schölkopf,et al.  Extracting Support Data for a Given Task , 1995, KDD.

[53]  Nina S. N. Lam,et al.  Fractals in Geography , 1993 .

[54]  P. An,et al.  Digital lithology mapping from airborne geophysical and remote sensing data in the Melville Peninsula, Northern Canada, using a neural network approach , 1995 .

[55]  B. Rockwell,et al.  Identification of quartz and carbonate minerals across northern Nevada using ASTER thermal infrared emissivity data—Implications for geologic mapping and mineral resource investigations in well-studied and frontier areas , 2008 .

[56]  José A. Sobrino,et al.  Satellite-derived land surface temperature: Current status and perspectives , 2013 .

[57]  D. Barber,et al.  SAR sea ice discrimination using texture statistics : a multivariate approach , 1991 .

[58]  Shunlin Liang,et al.  Fractal analysis of remotely sensed images: A review of methods and applications , 2006 .

[59]  H. M. Rajesh.  Mapping Proterozoic unconformity-related uranium deposits in the Rockhole area, Northern Territory, Australia using landsat ETM+ , 2008 .

[60]  Meiling Liu,et al.  Mafic-ultramafic and quartz-rich rock indices deduced from ASTER thermal infrared data using a linear approximation to the Planck function , 2014 .

[61]  Richard Gloaguen,et al.  Comparison of Different Machine Learning Algorithms for Lithological Mapping Using Remote Sensing Data and Morphological Features: A Case Study in Kurdistan Region, NE Iraq , 2015 .

[62]  Xiangnan Liu,et al.  Quartzose–mafic spectral feature space model: A methodology for extracting felsic rocks with ASTER thermal infrared radiance data , 2015 .

[63]  Richard Gloaguen,et al.  Remote sensing based improvement of the geological map of the Neoproterozoic Ras Gharib segment in the Eastern Desert (NE–Egypt) using texture features , 2015 .

[64]  M. Suzen,et al.  Mapping evaporate minerals by ASTER , 2011 .

[65]  Paul M. Mather,et al.  An evaluation of Landsat TM spectral data and SAR-derived textural information for lithological discrimination in the Red Sea Hills, Sudan , 1998 .

[66]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .