Evaluating pixel-based vs. object-based image analysis approaches for lithological discrimination using VNIR data of WorldView-3

Abstract

The object-based against pixel-based image analysis approaches were assessed for lithological mapping in a geologically complex terrain using Visible Near Infrared (VNIR) bands of WorldView-3 (WV-3) satellite imagery. The study area is Hormuz Island, southern Iran, a salt dome composed of dominant sedimentary and igneous rocks. When performing the object-based image analysis (OBIA) approach, the textural and spectral characteristics of lithological features were analyzed by the use of support vector machine (SVM) algorithm. However, in the pixel-based image analysis (PBIA), the spectra of lithological end-members, extracted from imagery, were used through the spectral angle mapper (SAM) method. Several test samples were used in a confusion matrix to assess the accuracy of classification methods quantitatively. Results showed that OBIA was capable of lithological mapping with an overall accuracy of 86.54% which was 19.33% greater than the accuracy of PBIA. OBIA also reduced the salt-and-pepper artifact pixels and produced a more realistic map with sharper lithological borders. This research showed limitations of pixel-based method due to relying merely on the spectral characteristics of rock types when applied to high-spatial-resolution VNIR bands of WorldView-3 imagery. It is concluded that the application of an object-based image analysis approach obtains a more accurate lithological classification when compared to a pixel-based image analysis algorithm.

Keywords

object-based image analysis / pixel-based image analysis / lithological mapping / Worldview-3 / Hormuz Island / spectral angle mapper / support vector machine

References

[1] Aqanabati S A (2006). Geology and mineral potential of Hormozgan Province. Journal of Development of Geology Training, 12: 4–11

[2] Ayoobi I, Tangestani M H (2018). Evaluation of subpixel unmixing algorithms in mapping the porphyry copper alterations using EO-1 Hyperion data, a case study from SE Iran. Remote Sensing Applications: Society and Environment, 10: 120–127

[3] Aufaristama M, Hölbling D, Höskuldsson A, Jónsdóttir I (2017). Comparison of SAM and OBIA as tools for lava morphology classification—a case study in Krafla, NE Iceland. In: EGU General Assembly, 19

[4] Blaschke T (2010). Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens, 65(1): 2–16

[5] Blaschke T, Strobl J (2001). What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS. GIS – Zeitschrift für Geoinformations systeme 14(6), 12–17

[6] Blaschke T, Burnett C, Pekkarinen A (2004). New contextual approaches using image segmentation for object-based classification. In: De Meer F, de Jong S, eds. Remote Sensing Image Analysis: Including the spatial domain. Dordrecht: Kluver Academic Publishers, 211–236

[7] Bedini B (2019). Application of WorldView-3 imagery and ASTER TIR data to map alteration minerals associated with the Rodalquilar gold deposits, southeast Spain. Adv Space Res, 63(10): 3346–3357

[8] Boardman J W, Kruse F A (1994). Automated spectral analysis: a geologic example using AVIRIS data, north Grapevine Mountains, Nevada. In: Tenth Thematic Conference on Geologic Remote Sensing, Environmental Research Institute of Michigan, Ann Arbor, MI

[9] Bahrambeygi B, Moeinzadeh H (2017). Comparison of support vector machine and neutral network classification method in hyperspectral mapping of ophiolite mélanges–a case study of east of Iran. Egypt J Remote Sens Space Sci, 20(1): 1–10

[10] Burges C J C, (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2: 121–167

[11] Bolouki S M, Ramazi H R, Maghsoudi A, Beiranvand Pour A, Sohrabi G (2019). A remote sensing-based application of bayesian networks for epithermal gold potential mapping in Ahar-Arasbaran Area, NW Iran. Remote Sens, 12(1): 105

[12] Benz U C, Hofmann P, Willhauck G, Lingenfelder I, Heynen M (2004). Multiresolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J Photogramm Remote Sens, 58(3–4): 239–258

[13] Congalton R G, Green K (2009). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton: CRC Press

[14] Castillejo-González I L, López-Granados F, García-Ferrer A, Peña-Barragán J M, Jurado-Expósito M, de la Orden M S, González-Audicana M (2009). Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery. Comput Electron Agric, 68(2): 207–215

[15] Clark R N, Roush T L (1984). Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications. J Geophys Res Solid Earth, 89(B7): 6329–6340

[16] Cai J, Luo J, Wang S, Yang S (2018). Feature selection in machine learning: a new perspective. Neurocomputing, 300: 70–79

[17] Dorren L K, Maier B, Seijmonsbergen A C (2003). Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification. Forest Ecology and Management, 183 (1–3): 31–46.

[18] Darwish A, Leukert K, Reinhardt W (2003). Image segmentation for the purpose of object-based classification. In: Geoscience and Remote Sensing Symposium, IGARSS’03. 2003 IEEE International, (3): 2039–2041

[19] Ducart D F, Silva A M, Toledo C L B, Assis L M (2016). Mapping iron oxides with Landsat-8/OLI and EO-1/ Hyperion imagery from the Serra Norte iron deposits in the Carajás Mineral Province, Brazil. Braz J Geol, 46(3): 331–349

[20] Elnagheeb A H, Bromley D W (1994). Extensification of agriculture and deforestation: empirical evidence from Sudan. Agricultural Economics 10(2): 193–200

[21] Fakhari M (1994). Geological Quadrangle Map of Bandar-Abbas, 1:250000. Sheet No. 1–13, National Iranian Oil Company

[22] Elyasi J, Aminsobhani E, Behzad A, Moeinvaziri H, Meysami A (1975). Geology of Hormuz Island. Geological Survey of Iran Publication, Tehran, 1: 13

[23] Grebby S, Field E, Tansey K (2016). Evaluating the Use of an Object-Based Approach to Lithological Mapping in Vegetated Terrain. Remote Sens, 8(10): 843

[24] Gasmi A, Gomez C, Zouari H, Masse A, Ducrot D (2016). PCA and SVM as geo-computational methods for geological mapping in the southern of Tunisia, using ASTER remote sensing data set. Arab J Geosci, 9(20): 753

[25] Huang C K, Kerr P F (1960). Infrared study of the carbonate minerals, Am Mineral, 45: 311–324

[26] Hunt G R (1980). Electromagnetic radiation: the communication link in remote sensing. In: Siega1 BS, Gillepie AR, eds, Remote Sensing in Geology, Wiley: New York, 5–45

[27] Hewson R D, Robson D, Carlton A, Gilmore P (2017). Geological application of ASTER remote sensing within sparsely outcropping terrain, Central New South Wales, Australia. Cogent Geosci, 3(1): 1319259

[28] Hay G J, Castilla G (2008). Geographic object-based image analysis (GEOBIA): a new name for a new discipline. In: Blaschke T, Lang S, Hay G, eds, Object Based Image Analysis. Heidelberg: Springer, 93–112

[29] Hay G J, Niemann K O (1994). Visualizing 3-D texture: a three dimensional structural approach to model forest texture. Can J Rem Sens, 20(2): 90–101

[30] Ibrahim E, Barnabé P, Ramanaidou E, Pirard E (2018). Mapping mineral chemistry of a lateritic outcrop in New Caledonia through generalized regression using Sentinel-2 and field reflectance spectra. Int J Appl Earth Obs Geoinformation, 73: 653–665

[31] Jensen J R (2005). Introductory Digital Image Processing. Upper Saddle River: Person Prentice Hall

[32] Karatzoglou A, Smola A, Hornik K, Zeileis A (2004). kernlab – an S4 package for kernel methods in R. J Stat Softw, 11(9): 1–20

[33] Kuester M (2016). Radiometric use of WV-3 imagery. Technical Note. DigitalGlobe

[34] Kavzoglu T, Colkesen I (2009). A kernel functions analysis for support vector machines for land cover classification. Int J Appl Earth Obs Geoinf, 11(5): 352–359

[35] Kruse F A, Lefkoff A B, Boardman J B, Heidebrecht K B, Shapiro A T, Barloon P J, Goetz A F H (1993). The Spectral Image Processing System (SIPS)–interactive visualization and analysis of imaging spectrometer data. Remote Sens Environ, 44(2-3): 145–163

[36] Lang S, Schöpfer E, Langanke T (2008). Combined object-based classification and manual interpretation — synergies for a quantitative assessment of parcels and biotopes. Geocarto Int, 23(4): 1–16

[37] Liu L, Feng J, Rivard B, Xu X, Zhou J, Han L, Yang J, Ren G (2018). Guangli Ren Mapping alteration using imagery from the Tiangong-1 hyperspectral spaceborne system: example for the Jintanzi gold province, China. Int J Appl Earth Obs Geoinf, 64: 275–286

[38] Marceau D (1999). The scale issue in the social and natural sciences. Can J Rem Sens, 25(4): 347–356

[39] Mahyari M G (2016). Comparison of ASTER reflective and thermal infrared data for discriminating lithological units and identifying potash-bearing areas in Hormoz salt plug. Dissertation for Master’s Degree. Shiraz: Shiraz University

[40] Matton G S, Canto F, Waldner S, Valero D, Morin J, Inglada M, Arias S, Bontemps B, Koetz P, Defourny P (2015). DefournyAn automated method for annual cropland mapping along the season for various globally-distributed agrosystems using high spatial and temporal resolution time series. Remote Sens, 7(10): 13208–13232

[41] Melgani F, Bruzzone L (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens, 42(8): 1778–1790

[42] Marceau D J, Howarth P J, Dubois J M M, Gratton D J (1990). Evaluation of the grey level co-occurrence matrix method for land-cover classification using SPOT imagery. IEEE Trans Geosci Remote Sens, 28(4): 513–519

[43] Mars J C (2018). Mineral and lithologic mapping capability of WorldView 3 data at Mountain Pass, California, using true- and false-color composite images, band ratios, and logical operator algorithms. Econ Geol, 113(7): 1587–1601

[44] Mason D C, Corr D G, Cross A, Hogg D C, Lawrence D H, Petrou M, Tailor A M (1988). The use of digital map data in the segmentation and classification of remotely-sensed images. Int J Remote Sens, 2: 195–215

[45] Mather P M, Tso B, Koch M (1998). An evaluation of Landsat TM spectral data and SAR-derived textural information for lithological discrimination in the Red Sea Hills, Sudan. Int J Remote Sens, 19(4): 587–604

[46] Markoski P R, Rolim S B A (2014). Evaluation of ASTER images for characterization and mapping of volcanic rocks (Basalts). International Journal of Advanced Remote Sensing and GIS, 3(1): 486–498

[47] Mengisteab B S, Blesius L, Hennessy L (2014). Application of object based image analysis (OBIA) in detection and quantifying forest loss caused by artisanal gold mining activities in Upper Mazaruni River Basin, Guyana. In: AGU Fall Meeting, 2014

[48] Noori L, Pour B A, Askari G, Taghipour N, Pradhan B, Lee C W, Honarmand M (2019). Comparison of different algorithms to map hydrothermal alteration zones using ASTER remote sensing data for polymetallic vein-type ore exploration: toroud–chahshirin magmatic belt (TCMB). North Iran. Remote Sensing, 11(5): 495

[49] Naghadehi K M, Hezarkhani A, Asadzadeh S (2014). Mapping the alteration footprint and structural control of Taknar IOCG deposit in east of Iran, using ASTER satellite data. Int J Appl Earth Obs Geoinf, 33: 57–66

[50] Oommen T, Misra D, Twarakavi N K C, Prakash A, Sahoo B, Bandopadhyay S (2008). An objective analysis of support vector machine based classification for remote sensing. Math Geosci, 40(4): 409–424

[51] Petitjean F, Kurtz C, Passat N, Gançarski P (2012). Spatio-temporal reasoning for the classification of satellite image time series. Pattern Recognit Lett, 33(13): 1805–1815

[52] Petropoulos G P, Vadrevu K, Kalaitzidis Ch (2012). Spectral Angle Mapper and Object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region. Geocarto Int, 28(2)

[53] Qiu F, Abdelsalam M, Thakkar P (2006). Spectral analysis of ASTER data covering part of the Neoproterozoic Allaqi-Heiani suture, Southern Egypt. J Afr Earth Sci, 44(2): 169–180

[54] Richards J A (1993). Remote Sensing Digital Image Analysis: An Introduction. 2nd ed. Berlin Heidelberg: Springer-Verlag

[55] Rajendran S, Nasir S (2019). ASTER capability in mapping of mineral resources of arid region: a review on mapping of mineral resources of the Sultanate of Oman. Ore Geol Rev, 108: 33–53

[56] Research Systems, Inc. (2002). ENVI Tutorials. Boulder: Research Systems, Inc

[57] Rajendran S, Nasir S, Kusky T M, Ghulam A, Gabr S, El Ghali M (2013). Detection of hydrothermal mineralized zones associated with listwaenites in Central Oman using ASTER data. Ore Geol Rev, 53: 470–488

[58] Stocklin J (1972). Lexique Stratigraphique International. Geological Survey of Iran Publication, Tehran, 36: 15

[59] Faramarzi N S, Amini S, Schmitt A K, Hassanzadeh J, Borg G, McKeegan K, Razavi S M H, Mortazavi S M (2015). Geochronology and geochemistry of rhyolites from Hormuz Island, southern Iran: a new record of Cadomian arc magmatism in the Hormuz Formation. Lithos, 236–237: 203–211

[60] Stocklin J (1974). Possible ancient continental margins in Iran. In: The Geology of Continental Margins. Berlin: Springer, 873–887

[61] Salisbury J W, Hunt G R (1974). Remote sensing of rock type in the visible and near infrared. In: Proceedings of 9th International Symposium on Remote Sensing Environment, Ann Arbor, vol III, 1953–1958

[62] Sun Y, Tian S, Di B (2017). Extracting mineral alteration information using Worldview-3 data. Geoscience Frontiers, 8(5): 1051–1062

[63] Segal D B (1983). Use of Landsat multispectral scanner data for the definition of limonitic exposures in heavily vegetated areas. Econ Geol, 78(4): 711–722

[64] Thapa R B, Murayama Y (2009). Urban mapping, accuracy, and image classification: a comparison of multiple approaches in Tsukuba City, Japan. Applied Geography 29:135–144

[65] Testa F J, Villanueva C, Cooke D, Zhang L (2018). Lithological and Hydrothermal Alteration Mapping of Epithermal, Porphyry and Tourmaline Breccia Districts in the Argentine Andes Using ASTER Imagery. Remote Sens, 10(2): 203

[66] Tormos T, Kosuth P, Durrieu S, Dupuy S, Villeneuve B, Wasson J (2012). Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data. Int J Remote Sens, 33(14): 4603–4633

[67] Trimble (2015). eCognition Developer 9.1 User Guide. Munich: Trimble Documentation

[68] Van der Werff H, Van Ruitenbeek F, Zegers T, Van der Meer F (2007). Geologic mapping on Mars by segmentation of OMEGA data. In: Proceedings 5th EARSeL Workshop on Imaging Spectroscopy

[69] Wang L, Sousa W, Gong P (2004). Integration of object-based and pixel-based classification for mangrove mapping with IKONOS imagery. Int J Remote Sens, 25(24): 5655–5668

[70] Ye B, Tian S, Ge J, Sun Y (2017). Assessment of WorldView-3 Data for Lithological Mapping. Remote Sens, 9(11): 1132

[71] Yazdi A, Arian M A, Tabari M M R (2014). Geological and geotourism study of Iran Geology Natural Museum, Hormoz Island. Open J Ecol, 4(11): 703–714

[72] Yeomans C, Middleton M, Shail R K, Grebby S, Lusty P A J (2019). Integrated Object-Based Image Analysis for semi-automated geological lineament detection in southwest England. Comput Geosci, 123: 137–148

RIGHTS & PERMISSIONS

Higher Education Press