Evaluation of a Feature Subset Selection method to find informative spectral bands of Hyperion hyperspectral data for hydrothermal alteration mapping: A case study from the Darrehzar porphyry copper mine, Kerman, Iran

Document Type : Research Article

Authors

• Amir Salimi ¹
• Mansour Ziaii ¹
• Ali Amiri ²
• Mahdieh Hosseinjani Zadeh ³

¹ Shahrood University of Technology

² Zanjan

³ Graduate University of Advanced Technology

10.22067/econg.v10i1.59704

Abstract

Introduction
In the regional prospecting of ore minerals, geologists usually utilize remote sensing images for hydrothermal alteration mineral mapping as a kind of lithological anomaly, which may be linked to mineral deposits (Carranza, 2002).
Compared to the multispectral remote sensing images, composed of few spectral bands, the hyperspectral data prepare much more spectral details of the surface materials in many bands. These high spectral resolution images provide subtle spectral data for identifying similar materials of the surface (Camps-Valls et al., 2014). This ability could greatly promote the potential of hyperspectral based mineral mapping (Wang and Zheng, 2010). As in the two last decades, hyperspectral remote sensing has been an important tool for studying earth’s minerals and rocks (Zhang and Peijun, 2014).
Although, the high number of spectral bands is an important advantage for hyperspectral images, many of those bands are usually irrelevant and redundant and, therefore, cause just the size and complexity of the band space to be increased. This complexity can lead to an ill-posed problem in supervised classification, namely the curse of dimensionality and the Hughes phenomenon, which negatively affect the accuracy of the classification (Camps-Valls, 2014).
Feature reduction methods can be applied to overcome these problems and to eliminate those spectral bands in the classification of hyperspectral images that provide no further useful information. These methods produce an efficient subset of features (spectral bands in remote sensing field) from the original feature space. The decrease in complexity obtained as a result of the feature space reduction can increase the ability of classifiers to efficiently capture the classification rules. Consequently, the speed, generalization, and predictive classification accuracy are increased (Gheyas and Smith, 2010; Camps-Valls et al., 2014).
This study is aimed at evaluation and management of the curse of dimensionality risk in hyper spectral data classification by means of a feature reduction method. The method is utilized to select more informative spectral bands of Hyperion hyperspectral data, which are more effective for the classification of hydrothermal alteration zones. The well-known study area here is the Darrehzar porphyry copper mine located 8 km from the southeast of the giant Sarcheshmeh mine.

Materials and methods
1. Hyperion data
The Hyperion hyperspectral image with 242 spectral bands acquired on July 26, 2004 was available and it was used in this study.
2. Train and test datasets
Two datasets were utilized. The first dataset that resulted from the Mixture Tuned Matched Filtering (MTMF) method was applied to feed the feature reduction method and the second dataset containing 17 rock samples collected from the study area was used to carry out the classification by SVM.
3. The feature reduction method
In this study, we applied a hybrid Feature Subset Selection (FSS) method to reduce the number of spectral bands of Hyperion data. Extensive details may be found in Moradkhani et al. (2015).

Discussion and results
The Feature Subset Selection (FSS) algorithm was applied to reduce the size of the spectral bands of Hyperion data. The implementation of this algorithm resulted in the selection of 9 bands among all 165 spectral bands (i.e. 5% of all useable spectral bands of Hyperion) as the more influential bands for the identification of clay minerals. These bands belong to the two spectral ranges, 2125-2250 nm and 2250-2400 nm, respectively. On the other hand, it is believed that the Short-Wave Infrared (SWIR) electromagnetic range (2000-2500 nm) is an important spectral range for distinguishing clay minerals of the hydrothermal alteration systems (Hosseinjani Zadeh et al., 2014). This implies that two ranges introduced by FSS were accurately selected, because both of them coincide with the SWIR range. Clearly speaking, bands 201, 202, 204, and 205 in the range of 2125-2250 nm are used for muskovit, kaolinit and alunit enhancement. Moreover, bands 217, 220, 222, 223, and 224 in the 2250-2400 nm are appropriate for chlorite classification.
A comparison between the maps of SVM based classification of the alteration zones using 9 (selected by feature selection method) and 165 (all useable bands of Hyperion data) spectral bands confirmed a significant improvement in the output results when 9 more informative bands are utilized for classification instead of all 165 bands. In fact, the classification based on 9 selected bands is comparable and even more effective than the full band classification. This is because the decrease in spectral bands makes SVM learn the rules of classification more accurately.

Reference
Camps-Valls, G., Tuia, D., Bruzzone, L. and Benediktsson, J., 2014. Advances in Hyperspectral Image Classification. IEEE Signal Processing Magazine, 31(1): 45–54.
Carranza, E.J.M., 2002. Geologically-Constrained Mineral Potential Mapping. Ph.D. Thesis, Delft University of Technology, Delft, Netherlands, 480 pp.
Gheyas, A. and Smith, L.S., 2010. Feature subset selection in large dimensionality domains. Pattern Recognition, 43(1): 5-13.
Hosseinjani Zadeh, M., Tangestani, M.H., Velasco Roldan, F. and Yusta. I., 2014. Spectral characteristics of minerals in alteration zones associated with porphyry copper deposits in the middle part of Kerman copper belt, SE Iran., SE Iran. Ore Geology Reviews, 62: 191-198.
Moradkhani, M., Amiri, A., Javaherian, M. and Safari, H., 2015. A hybrid algorithm for feature subset selection in high-dimensional data sets using FICA and IWSSr algorithm. Applied Soft Computing, 35: 123–135.
Wang, Z.H. and Zheng, C.Y., 2010. Rocks/Minerals Information Extraction from EO-1 Hyperion Data Base on SVM. International Conference on Intelligent Computation Technology and Automation, Changsha, China.
Zhang, X. and Peijun, L., 2014. Lithological mapping from hyperspectral data by improved use of spectral angle mapper. International Journal of Applied Earth Observation and Geoinformation, 31: 95–109.

Keywords

• Feature subset selection method
• Alteration zones
• Hyperspectral remote sensing
• Darrehzar porphyry type copper deposit

20.1001.1.20087306.1397.10.1.5.8

---

References

Alajlan, N., Bazi, Y., Melgani, F. and Yager, R., 2012. Fusion of supervised and unsupervised learning for improved classification of hyperspectral images. Journal of Information Sciences, 217: 39–55.

Alavi, M., 1980. Tectono-stratigraphic evolution of the Zagros side of Iran. Geology, 8(3): 144–149.

Amer, R., Kusky, T. and El Mezayen, A., 2012. Remote sensing detection of gold related alteration zones of Um Rus Area, Central Eastern Desert of Egypt. Advances in Space Research, 49(1): 121–134.

Beiranvand Pour, A. and Hashim, M., 2014. ASTER, ALI and Hyperion sensors data for lithological mapping and ore minerals exploration. SpringerPlus, 3(1): 1–19.

Bermejo, P., de la Ossa, L., Gamez, J.A. and Puerta, J.M., 2012. Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking. Knowledge-based Systems, 25(1): 35–44.

Bioucas-Dias, J.M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N.M. and Chanussot, J., 2013. Hyperspectral Remote Sensing Data Analysis and Future Challenges. IEEE Geoscience and Remote Sensing Magazine, 1(2): 6–36.

Camps-Valls, G., Tuia, D., Bruzzone, L. and Benediktsson, J., 2014. Advances in Hyperspectral Image Classification. IEEE Signal Processing Magazine, 31(1): 45–54.

Carranza, E.J.M., 2002. Geologically-Constrained Mineral Potential Mapping. Ph.D. Thesis, Delft University of Technology, Delft, Netherlands, 480 pp.

Chang, C., 2007. Hyperspectral Data Exploitation, Theory and Applications. John Wiley & Sons, New Jersey, 430 pp.

Darmawan, A., 2006. Mapping soil mineral using Hyperion imagery in relation to the level of structural damage in the Bam earthquake. M.Sc. Thesis, the University of Melbourne, Melbourne, Australia, 86 pp.

Ede, R.V., 2004. Destriping and Geometric Correction of an ASTER Level 1A Image. Utrecht University, Utrecht, 36 pp.

Fukunaga, K., 1990. Statistical Pattern Recognition. Academic Press, San Diego, 592 pp.

Geological survey of Iran, 1973. Exploration for Ore deposits in Kerman region. Ministry of Economy Geological Survey of Iran, Tehran, Report Yu/53.

Gersman, R., Ben-Dor, E., Beyth, M., Avigad, D., Abraha, M. and Kibreba, A., 2008. Mapping of hydrothermal altered rocks by the EO-1 Hyperion sensor, northern Danakil, Eritrea. International Journal of Remote Sensing, 29(13): 3911–3936.

Gheyas, A. and Smith, L.S., 2010. Feature subset selection in large dimensionality domains. Pattern Recognition, 43(1): 5-13.

Hosseinjani Zadeh, M. and Tangestani, M.H., 2011. Mapping alteration minerals using sub-pixel unmixing of ASTER data in the Sarduiyeh area, southeastern Kerman Iran. International Journal of Digital Earth, 4(6): 487–504.

Hosseinjani Zadeh, M., Tangestani, M.H., Velasco Roldan, F. and Yusta, I., 2014a. Sub-pixel mineral mapping of a porphyry copper belt using EO-1 Hyperion data. Advances in Space Research, 53(3): 440–451.

Hosseinjani Zadeh, M., Tangestani, M.H., Velasco Roldan, F. and Yusta, I., 2014b. Mineral exploration and alteration zone mapping using mixture tuned matched filtering approach on ASTER data at the central part of Dehaj-Sarduiyeh copper belt, SE Iran. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1): 284–289.

Hosseinjani Zadeh, M., Tangestani, M.H., Velasco Roldan, F. and Yusta. I., 2014c. Spectral characteristics of minerals in alteration zones associated with porphyry copper deposits in the middle part of Kerman copper belt, SE Iran., SE Iran. Ore Geology Reviews, 62: 191-198.

Hui-Huang, H. and Cheng-Wei, H., 2011. Hybrid feature selection by combining filters and wrappers. Expert Systems with Applications, 38(7): 8144–8150.

Jimenez, L.O. and Landgrebe, D.A., 1998. Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data. IEEE Transactions on Systems, Man, and Cybernetics, 28(1): 39–54.

Kruse, F.A., 2003. Mineral Mapping with AVIRIS and EO-1 Hyperion. 12th JPL Airborne Geoscience Workshop, Pasadena, California.

Landgrebe, D.A., 2002. Hyperspectral Image Data Analysis. IEEE Signal processing Magazine, 19(1): 17–28.

Lazar, C., Taminau, J., Meganck, S., Steenhoff, D., Coletta, A., Molter, C., de Schaet-zen, V., Duque, R., Bersini, H. and Nowe, A., 2012. A survey on filter techniques for feature selection in gene expression microarray analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(4): 1106–1119.

Liao, L., Jarecke, P., Gleichauf, D. and Hedman, T., 2000. Performance and characterization of the Hyperion imaging spectrometer instrument. International Symposium on Optical Science and Technology, San Diego, California, USA.

Moradkhani, M., Amiri, A., Javaherian, M. and Safari, H., 2015. A hybrid algorithm for feature subset selection in high-dimensional data sets using FICA and IWSSr algorithm. Applied Soft Computing, 35: 123–135.

Oommen, T., 2008. An objective analysis of Support Vector Machine based classification for remote sensing. Mathematical Geosciences, 40(4): 409–424.

Pal, M. and Foody, G.M., 2010. Feature Selection for Classification of Hyperspectral Data by SVM. IEEE Transactions on Geoscience and Remote Sensing, 48(5): 2297–2307.

Petropoulos, G.P., Kalaitzidis, C. and Prasad Vadrevu, K., 2012. Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery. Computers & Geosciences, 41: 99–107.

Plaza, A., Benediktsson, J.A., Boardman, J.W., Brazile, J., Bruzzone, L., Camps-Valls, G., Chanussot, J., Fauvel, M., Gamba, P., Gualtieri, A., Marconcini, M., Tilton, J.C. and Trianni, G., 2009. Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment, 113: 110–122.

Ranjbar, H., Hassanzadeh, H. and Torabi, M., 2001. Integration and analysis of airborne geophysical data of the Darrehzar area, Kerman Province, Iran, using principal component analysis. Journal of Applied Geophysics, 48(1): 33–41.

Salimi, A., Ziaii, M., Amiri, A. and Hosseinjani Zadeh, M., 2016. Sub-pixel classification of hydrothermal alteration zones using a kernel-based method and hyperspectral data; A case study of Sarcheshmeh Porphyry Copper Mine and surrounding area, Kerman, Iran. Journal of Mining and Environment, 8(4): 555-565.

Salimi, A., Ziaii, M., Hosseinjani Zadeh, M., Amiri, A. and Karimpouli, S., 2015. High Performance of the Support Vector Machine in Classifying Hyperspectral Data Using a Limited Dataset. International Journal of Mining and Geo-Engineering, 49(2): 253–268.

Shahriari, H., Honarmand, M. and Ranjbar, H., 2015. Comparison of multi-temporal ASTER images for hydrothermal alteration mapping using a fractal-aided SAM method. International Journal of Remote Sensing, 36(5): 1271–1289.

Shahriari, H., Ranjbar, H. and Honarmand, M., 2013. Image Segmentation for Hydrothermal Alteration Mapping Using PCA and Concentration-Area Fractal Model. Natural Resources Research, 22(3): 191–206.

Van der Meer, F., Van der Werff, H., Van Ruitenbeek, F., Hecker, C., Bakker, W., Noomen, M., Van der Meijde, M., Carranza, E., Boudewijn de Smeth, J. and Woldai, T., 2012. Multi- and hyperspectral geologic remote sensing: A review. International Journal of Applied Earth Observation and Geoinformation, 14(1): 112–128.

Wang, J. and Wu, L., 2013. Maximum weight and minimum redundancy: A novel framework for feature subset selection. Pattern Recognition, 46(6): 1616–1627.

Wang, Z.H. and Zheng, C.Y., 2010. Rocks/Minerals Information Extraction from EO-1 Hyperion Data Base on SVM. International Conference on Intelligent Computation Technology and Automation, Changsha, China.

Waske, B., Benediktsson, J.A., Arnason, K. and Sveinsson, J.R., 2009. Mapping of hyperspectral AVIRIS data using machine-learning algorithms. Canadian Journal of Remote Sensing, 35(1): 106–116.

Yusta, S.C., 2009. Different metaheuristic strategies to solve the feature selection problem. Pattern Recognition Letter, 30(5): 525–534.

Zhang, X. and Peijun, L., 2014. Lithological mapping from hyperspectral data by improved use of spectral angle mapper. International Journal of Applied Earth Observation and Geoinformation, 31: 95–109.