A Comprehensive Review on Pixel Oriented and Object Oriented Methods for Information Extraction from Remotely Sensed Satellite Images with a Special Emphasis on Cryospheric Applications

Abstract

Image classification is one of the most basic operations of digital image processing. The present review focuses on the strengths and weaknesses of traditional pixel-based classification (PBC) and the advances of object-oriented classification (OOC) algorithms employed for the extraction of information from remotely sensed satellite imageries. The state-of-the-art classifiers are reviewed for their potential usage in urban remote sensing (RS), with a special focus on cryospheric applications. Generally, classifiers for information extraction can be divided into three catalogues: 1) based on the type of learning (supervised and unsupervised), 2) based on assumptions on data distribution (parametric and non-parametric) and, 3) based on the number of outputs for each spatial unit (hard and soft). The classification methods are broadly based on the PBC or the OOC approaches. Both methods have their own advantages and disadvantages depending upon their area of application and most importantly the RS datasets that are used for information extraction. Classification algorithms are variedly explored in the cryosphere for extracting geospatial information for various logistic and scientific applications, such as to understand temporal changes in geographical phenomena. Information extraction in cryospheric regions is challenging, accounting to the very similar and conflicting spectral responses of the features present in the region. The spectral responses of snow and ice, water, and blue ice, rock and shadow are a big challenge for the pixel-based classifiers. Thus, in such cases, OOC approach is superior for extracting information from the cryospheric regions. Also, ensemble classifiers and customized spectral index ratios (CSIR) proved extremely good approaches for information extraction from cryospheric regions. The present review would be beneficial for developing new classifiers in the cryospheric environment for better understanding of spatial-temporal changes over long time scales.

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Jawak, S. , Devliyal, P. and Luis, A. (2015) A Comprehensive Review on Pixel Oriented and Object Oriented Methods for Information Extraction from Remotely Sensed Satellite Images with a Special Emphasis on Cryospheric Applications. Advances in Remote Sensing, 4, 177-195. doi: 10.4236/ars.2015.43015.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Campbell, J.B. and Wynne, R.H. (2011) Introduction to Remote Sensing. 5th Edition.
http://www.guilford.com/books/Introduction-to-Remote-Sensing/Campbell-Wynne/9781609181765
[2] Lu, D. and Weng, Q. (2007) A Survey of Image Classification Methods and Techniques for Improving Classification Performance. International Journal of Remote Sensing, 28, 823-870.
http://dx.doi.org/10.1080/01431160600746456
[3] Tso, B. and Mather, P.M. (2001) Classification Methods for Remotely Sensed Data. Taylor and Francis Inc., New York.
http://dx.doi.org/10.4324/9780203303566
[4] Landgrebe, D.A. (2003) Signal Theory Methods in Multispectral Remote Sensing. John Wiley and Sons, Hoboken.
http://dx.doi.org/10.1002/0471723800
[5] Kumar, M. and Singh, R.K. (2013) Digital Image Processing of Remotely Sensed Satellite Images for Information Extraction. Conference on Advances in Communication and Control Systems (CAC2S 2013).
[6] Gong, P. and Howarth, P.J. (1992) Frequency-Based Contextual Classification and Gray-Level Vector Reduction for Land-Use Identification. Photogrammetric Engineering and Remote Sensing, 58, 423-437.
http://nature.berkeley.edu/~penggong/PDFpapers/GongPERS92.pdf
[7] Kontoes, C., Wilkinson, G.G., Burrill, A., Goffredo, S. and Megier, J. (1993) An Experimental System for the Integration of GIS Data in Knowledge-Based Image Analysis for Remote Sensing of Agriculture. International Journal of Geographical Information Systems, 7, 247-262.
http://dx.doi.org/10.1080/02693799308901955
[8] Foody, G.M. (1996) Approaches for the Production and Evaluation of Fuzzy Land Cover Classification from Remotely-Sensed Data. International Journal of Remote Sensing, 17, 1317-1340.
http://dx.doi.org/10.1080/01431169608948706
[9] San Miguel-Ayanz, J. and Biging, G.S. (1997) Comparison of Single-Stage and Multi-Stage Classification Approaches for Cover Type Mapping with TM and SPOT Data. Remote Sensing of Environment, 59, 92-104.
http://dx.doi.org/10.1016/S0034-4257(96)00109-5
[10] Aplin, P., Atkinson, P.M. and Curran, P.J. (1999) Per-Field Classification of Land Use Using the Forthcoming Very Fine Spatial Resolution Satellite Sensors: Problems and Potential Solutions. In: Atkinson, P.M. and Tate, N.J., Eds., Advances in Remote Sensing and GIS Analysis, John Wiley and Sons, New York, 219-239.
[11] Stuckens, J., Coppin, P.R. and Bauer, M.E. (2000) Integrating Contextual Information with Per-Pixel Classification for Improved Land Cover Classification. Remote Sensing of Environment, 71, 282-296.
http://dx.doi.org/10.1016/S0034-4257(99)00083-8
[12] Franklin, S.E. and Wulder, M.A. (2002) Remote Sensing Methods in Medium Spatial Resolution Satellite Data Land Cover Classification of Large Areas. Progress in Physical Geography, 26, 173-205.
http://dx.doi.org/10.1191/0309133302pp332ra
[13] Pal, M. and Mather, P.M. (2003) An Assessment of the Effectiveness of Decision Tree Methods for Land Cover Classification. Remote Sensing of Environment, 86, 554-565.
http://dx.doi.org/10.1016/S0034-4257(03)00132-9
[14] Gallego, F.J. (2004) Remote Sensing and Land Cover Area Estimation. International Journal of Remote Sensing, 25, 3019-3047.
http://dx.doi.org/10.1080/01431160310001619607
[15] Arbiol, R., Zhang, Y. and Palà, V. (2006) Advanced Classification Techniques: A Review. Proceedings of the ISPRS Commission VII Symposium, Enschede, 8-11 May 2006.
http://www.isprs.org/proceedings/XXXVI/part7/PDF/250.pdf
[16] Quackenbush, L.J. (2004) A Review of Techniques for Extracting Linear Features from Imagery. Photogrammetric Engineering & Remote Sensing, 70, 1383-1392.
http://dx.doi.org/10.14358/PERS.70.12.1383
[17] Li, M., Zang, S., Zhang, B., Li, S. and Wu, C. (2014) A Review of Remote Sensing Image Classification Techniques: The Role of Spatio-Contextual Information. European Journal of Remote Sensing, 47, 389-411.
http://dx.doi.org/10.5721/EuJRS20144723
[18] Mountrakis, G., Im, J. and Ogole, C. (2011) Support Vector Machines in Remote Sensing: A Review. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 247-259.
http://dx.doi.org/10.1016/j.isprsjprs.2010.11.001
[19] Jawak, S.D., Luis, A.J., Panditrao, S.N., Khopkar, P.S. and Jadhav, P.S. (2013) Advancement in Landcover Classification Using Very High Resolution Remotely Sensed 8-Band WorldView-2 Satellite Data. International Journal of Earth Sciences and Engineering, 6, 1742-1749.
[20] Jawak, S.D. and Luis, A.J. (2011) Applications of WorldView-2 Satellite Data for Extraction of Polar Spatial Information and DEM of Larsemann Hills, East Antarctica. International Conference on Fuzzy Systems and Neural Computing (FSNC 2011), Hong Kong, 20-21 February 2011, Vol. 3, 148-151.
[21] Blaschke, T. (2010) Object Based Image Analysis for Remote Sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65, 2-16.
http://dx.doi.org/10.1016/j.isprsjprs.2009.06.004
[22] Blaschke, T., Hay, G.J., Kelly, M., Lang, S., Hofmann, P., Addink, E., Feitosa, R.Q., Van der Meer, F., Van der Werff, H., Van Coillie, F. and Tiede, D. (2014) Geographic Object-Based Image Analysis—Towards a New Paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 180-191.
http://dx.doi.org/10.1016/j.isprsjprs.2013.09.014
[23] Yongxue, L., Manchun, L., Liang, M., Feifei, X. and Shuo, H. (2006) Review of Remotely Sensed Imagery Classification Patterns Based on Object-Oriented Image Analysis. Chinese geographical Science, 16, 282-288.
http://dx.doi.org/10.1007/s11769-006-0282-0
[24] Myint, S.W., Gober, P., Brazel, A., Grossman-Clarke, S. and Weng, Q. (2011) Per-Pixel vs. Object-Based Classification of Urban Land Cover Extraction Using High Spatial Resolution Imagery. Remote Sensing of Environment, 115, 1145-1161.
http://dx.doi.org/10.1016/j.rse.2010.12.017
[25] Cetin, M., Kavzoglu, T. and Musaoglu, N. (2004) Classification of Multi-Spectral, Multi-Temporal and Multi-Sensor images Using Principal Components Analysis and Artificial Neural Networks: Beykoz Case. Proceedings of 20th Congress of the International Society of Photogrammetry and Remote Sensing, Istanbul, 12-23 July 2004.
http://www.isprs.org/proceedings/XXXV/congress/comm4/papers/480.pdf
[26] Bogang, Y. and Yu, X. (2014) Remote Sensing Image Classification of Geoeye-1 High-Resolution Satellite. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS Technical Com- mission IV Symposium, Suzhou, 14-16 May 2014, Vol. XL-4, 325.
[27] Jabari, S. and Zhang, Y. (2013) Very High Resolution Satellite Image Classification Using Fuzzy Rule-Based Systems. Algorithms, 6, 762-781.
http://dx.doi.org/10.3390/a6040762
[28] Aguilar, M.A., Bianconi, F., Aguilar, F.J. and Fernández, I. (2014) Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery. Remote Sensing 2014, 6, 3554-3582.
http://dx.doi.org/10.3390/rs6053554
[29] Cheung-Wai Chan, J., Demarchi, L., Van de Voorde, T. and Canters, F. (2008) Binary Classification Strategies for Mapping Urban Land Cover with Ensemble Classifiers. Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Boston, July 6-11, Vol. 3, 1004-1007.
[30] Gil, A., Yu, Q., Lobo, A., Lourenco, P., Silva, L. and Calado, H. (2011) Assessing the Effectiveness of High Resolution Satellite Imagery for Vegetation Mapping in Small Islands Protected Areas. Journal of Coastal Research, Special Issue 64, 1663-1667.
[31] Clasen, A., Frick, A. and Kleinschmit, B. (2006) Knowledge Based Methods for the Classification of Forest Parameters with QuickBird Data. Proceedings of the 2nd Workshop of the EARSeL SIG on Land Use and Land Cover, Center for Remote Sensing of Land Surfaces, Bonn, 28-30 September 2006, 269-275.
http://www.zfl.uni-bonn.de/research/earsel/
special-interest-group-on-land-use/269-275_clasen.pdf
[32] Muslim, A.M., David, D., Bachok, Z. and Sabri, W.A. (2011) An Ensemble of Classifiers Approach to Coral Distribution Mapping. 34th International Symposium on Remote Sensing of Environment, Sydney, 10-15 April 2011.
http://www.isprs.org/proceedings/2011/isrse-34/211104015Final00286.pdf
[33] Kux, H.J.H. and Souza, U.D.V. (2012) Object Based Image Analysis of WorldView-2 Satellite Data for the Classification of Mangrove Areas in the City of Sao Luis, Maranhao State, Brazil. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, I-4, 95-100.
http://dx.doi.org/10.5194/isprsannals-I-4-95-2012
[34] Chen, Z., Ning, X. and Zhang, J. (2012) Urban Land Cover Classification Based on WorldView-2 Image Data. IEEE International Symposium on Geomatics for Integrated Water Resource Management, Lanzhou, 19-21 October 2012, 1-5.
[35] Lewinski, St. and Bochenek, Z. (2008) Rule-Based Classification of SPOT Imagery Using Object-Oriented Approach for Detailed Land Cover Mapping. Proceedings of 28th EARSeL Symposium, Remote Sensing for a Changing Europe, Istanbul, 2-5 June 2008.
http://www.ecognition.com/sites/default/files/1098_lewinski_bochenek_rule_based_classification_
of_spot_imagery_for_landcover_mapping.pdf
[36] Akgün, A., Eronat, A.H. and Türk, N. (2004) Comparing Different Satellite Image Classification Methods: An Application in Ayvalik District, Western Turkey. 20th ISPRS Congress Technical Commission IV, Istanbul, 12-23 July 2004, 1091-1097.
http://www.isprs.org/proceedings/xxxv/congress/comm4/papers/505.pdf
[37] Al-Ahmadi, F.S. and Hames, A.S. (2009) Comparison of Four Classification Methods to Extract Land Use and Land Cover from Raw Satellite Images for Some Remote Arid Areas, Kingdom of Saudi Arabia. Journal of King Abdulaziz University-Earth Sciences, 20, 167-191.
http://dx.doi.org/10.4197/ear.20-1.9
[38] Jawak, S.D. and Luis, A.J. (2013) Very High-Resolution Satellite Data for Improved Land Cover Extraction of Larsemann Hills, Eastern Antarctica. Journal of Applied Remote Sensing, 7, Article ID: 073460.
[39] Novack, T., Esch, T., Kux, H. and Stilla, U. (2011) Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification. Remote Sensing 2011, 3, 2263-2282.
http://dx.doi.org/10.3390/rs3102263
[40] Giada, S., De Groeve, T. and Ehrlich, D. (2003) Information Extraction from Very High Resolution Satellite Imagery Over Lukole Refugee Camp, Tanzania. International Journal of Remote Sensing, 24, 4251-4266.
http://dx.doi.org/10.1080/0143116021000035021
[41] Shaker, A., Yan, W.Y. and El-Ashmawy, N. (2012) Panchromatic Satellite Image Classification for Flood Hazard Assessment. Journal of Applied Research and Technology, 10, 902-911.
http://www.redalyc.org/articulo.oa?id=47425142010
[42] Lewis, D., Phinn, S. and Pfitzner, K. (2012) Pixel-Based Image Classification to Map Vegetation Communities Using SPOT5 and Landsat TM in a Northern Territory Tropical Savanna, Australia. Canadian Journal of Remote Sensing, 38, 5, 570-585.
http://dx.doi.org/10.5589/m12-047
[43] Pradhan, R., Ghose, M.K. and Jeyaram, A. (2010) Land Cover Classification of Remotely Sensed Satellite Data Using Bayesian and Hybrid Classifier. International Journal of Computer Applications (0975-8887), 7, 1-4.
http://dx.doi.org/10.5120/1295-1783
[44] Hamedianfar, A., Mohd Shafri, H.Z., Mansor, S. and Ahmad, N. (2014) Detailed Urban Object-Based Classifications from WorldView-2 Imagery and LiDAR Data: Supervised vs. Fuzzy Rule-Based. FIG Congress 2014, Engaging the Challenges—Enhancing the Relevance, Kuala Lumpur, 16-21 June 2014.
[45] Jawak, S.D., Raut, D.A. and Luis, A.J. (2015) Iterative Spectral Index Ratio Exploration for Object-Based Image Analysis of Antarctic Coastal Oasis Using High Resolution Satellite Remote Sensing Data. ICWRCOE 2015, Aquatic Procedia, 4, 157-164.
http://dx.doi.org/10.1016/j.aqpro.2015.02.022
[46] Sun, X.X., Zhang, J.X. and Liu, Z.J. (2005) A Comparison of Object-Oriented and Pixel-Based Classification Approachs Using Quickbird Imagery. ISPRS Proceedings, XXXVI-2/W25.
http://www.isprs.org/proceedings/XXXVI/2-W25/source/A_COMPARISON_OF_OBJECT-ORIENTED_AND_PIXEL-
BASED_CLASSIFICATION_APPROACHS_USING_QUICKBIRD.pdf
[47] Whiteside, T. and Ahmad, W. (2005) A Comparison of Object-Oriented and Pixel-Based Classification Methods for Mapping Land Cover in Northern Australia. Proceedings of SSC2005 Spatial intelligence, Innovation and Praxis: The National Biennial Conference of the Spatial Sciences Institute, Melbourne, September 2005, 1225-1231.
http://www.ecognition.com/sites/default/files/273_0168.pdf
[48] Marangoz, A.M., Oruc, M., Karakis, S. and Sahin, H. (2004) Comparison of Pixel-Based and Object-Oriented Classification Using IKONOS Imagery for Automatic Building Extraction—Safranbolu Testfield. Proceedings of the 20th ISPRS Congress, Istanbul, 12-23 July 2004.
http://jeodezi.beun.edu.tr/marangoz/files/2013/03/bildiri21.pdf
[49] Riggan Jr., N.D. and Weih Jr., R.C. (2009) A Comparison of Pixel-Based versus Object-Based Land Use/Land Cover Classification Methodologies. Journal of the Arkansas Academy of Science, 63, 145-152.
http://libinfo.uark.edu/aas/issues/2009v63/v63a16.pdf
[50] Gholoobi, M., Tayyebi, A., Taleyi, M. and Tayyebi, A.H. (2010) Comparing Pixel Based and Object Based Approaches in Land Use Classification in Mountainous Areas. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, Kyoto Japan, Vol. XXXVIII, 789-794.
http://www.isprs.org/proceedings/xxxviii/part8/pdf/W08L35_20100302025739.pdf
[51] Lu, D. and Weng, Q. (2004) Spectral Mixture Analysis of the Urban Landscape in Indianapolis with Landsat ETM+ Imagery. Photogrammetric Engineering and Remote Sensing, 70, 1053-1062.
http://dx.doi.org/10.14358/PERS.70.9.1053
[52] Sridharan, H. and Qiu, F. (2013) Developing an Object Based Hyperspectral Image Classifier with a Case Study Using WorldView-2 Data. Photogrammetric Engineering & Remote Sensing, 79, 1027-1036.
http://dx.doi.org/10.14358/PERS.79.11.1027
[53] Aplin, P. and Smith, G.M. (2008) Advances in Object Based Image Classification. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Beijing, Vol. 37, 725-728.
http://www.isprs.org/proceedings/XXXVII/congress/7_pdf/4_WG-VII-4/45.pdf
[54] Corcoran, P., Winstanley, A. and Mooney, P. (2010) Segmentation Performance Evaluation for Object Based Remotely Sensed Image Analysis. International Journal of Remote Sensing, 31, 617-645.
http://dx.doi.org/10.1080/01431160902894475
[55] Liu, D. and Xia, F. (2010) Assessing Object-Based Classification: Advantages and Limitations. Remote Sensing Letters, 1, 187-194.
http://dx.doi.org/10.1080/01431161003743173
[56] Kim, S.H. and Hong, C.H. (2012) Antarctic Land-Cover Classification Using IKONOS and Hyperion Data at Terra Nova Bay. International Journal of Remote Sensing, 33, 7151-7164.
http://dx.doi.org/10.1080/01431161.2012.700136
[57] Kim, Y.S., et al. (2010) Development of Site Investigation and Monitoring System for Extreme Cold Region. Korea Institute of Construction Technology.
http://nysmac.npolar.no/37-Meeting-Groningen/StatusReports37.pdf
[58] Jawak, S.D. and Luis, A.J. (2013) A Spectral Index Ratio-Based Antarctic Land-Cover Mapping Using Hyperspatial 8-Band WorldView-2 Imagery. Polar Science, 7, 18-38.
http://dx.doi.org/10.1016/j.polar.2012.12.002
[59] Liu, C.C., Chang, Y.C., Huang, S., Yan, S.Y., Wu, F., Wu, A.M., Kato, S. and Yamaguchi, Y. (2009) Monitoring the Dynamics of Ice Shelf Margins in Polar Regions with High-Spatial- and High-Temporal-Resolution Space-Borne Optical Imagery. Cold Regions Science and Technology, 55, 14-22.
http://dx.doi.org/10.1016/j.coldregions.2008.07.008
[60] Yang, K. and Smith, L.C. (2013) Supraglacial Streams on the Greenland Ice Sheet Delineated from Combined Spectral-Shape Information in High-Resolution Satellite Imagery. IEEE Geoscience and Remote Sensing Letters, 10, 801- 805.
[61] Paul, F. (2000) Evaluation of Different Methods for Glacier Mapping Using Landsat TM. Proceedings of EARSeL-SIG-Workshop Land Ice and Snow, Dresden/FRG, 16-17 June 2000.
http://web.unbc.ca/~bolch/publications/BolcKamp06_GeoRaum.pdf
[62] Shukla, A., Arora, M.K. and Gupta, R.P. (2010) Synergistic Approach for Mapping Debris-Covered Glaciers Using Optical-Thermal Remote Sensing Data with Inputs from Geomorphometric Parameters. Remote Sensing of Environment, 114, 1378-1387.
http://dx.doi.org/10.1016/j.rse.2010.01.015
[63] Adam, S., Pietroniro, A. and Brugman, M.M. (1997) Glacier Snow Line Mapping Using ERS-1 SAR Imagery. Remote Sensing of Environment, 61, 46-54.
http://dx.doi.org/10.1016/S0034-4257(96)00239-8
[64] Bolch, T. and Kamp, U. (2006) Glacier Mapping in High Mountains Using DEMs, Landsat and ASTER Data. 8th International Symposium on High Mountain Remote Sensing Cartography, La Paz, 21-27 March 2005, 37-48.
http://web.unbc.ca/~bolch/publications/BolcKamp06_GeoRaum.pdf
[65] Hui, F., Ci, T., Cheng, X., Scambos, T.A., Liu, Y., Zhang, Y., Chi, Z., Huang, H., Wang, X., Wang, F., Zhao, C., Jin, Z. and Wang, K. (2014) Mapping Blue-Ice Areas in Antarctica Using ETM+ and MODIS Data. Annals of Glaciology, 55, 129-137.
http://dx.doi.org/10.3189/2014AoG66A069
[66] Paul, F., Huggel, C. and Kaab, A. (2004) Combining Satellite Multispectral Image Data and a Digital Elevation Model for Mapping Debris-Covered Glaciers. Remote Sensing of Environment, 89, 510-518.
http://dx.doi.org/10.1016/j.rse.2003.11.007
[67] Roshani, N., Valadan Zouj, M.J., Rezaei, Y. and Nikfar, M. (2008) Snow Mapping of Alamchal Glacier Using Remote Sensing Data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Beijing, Vol. XXXVII, 805-808.
http://www.isprs.org/proceedings/XXXVII/congress/8_pdf/7_WG-VIII-7/14.pdf
[68] Fang, L., Maksymiuk, O., Schmitt, M. and Stilla, U. (2013) Determination of Glacier Surface Area Using Spaceborne SAR Imagery. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS Hannover Workshop 2013, Hannover, 21-24 May 2013, Vol. XL-1/W1, 105-110.
http://dx.doi.org/10.5194/isprsarchives-xl-1-w1-105-2013
[69] Ghosh, S., Pandey, A.C. and Nathawat, M.S. (2014) Mapping of Debris-Covered Glaciers in Parts of the Greater Himalaya Range, Ladakh, Western Himalaya, Using Remote Sensing and GIS. Journal of Applied Remote Sensing, 8.
http://dx.doi.org/10.1117/1.JRS.8.083579
[70] Jawak, S.D. and Luis, A.J. (2013) A Comprehensive Evaluation of PAN-Sharpening Algorithms Coupled with Resampling Methods for Image Synthesis of Very High Resolution Remotely Sensed Satellite Data. Advances in Remote Sensing, 2, 332-344.
http://dx.doi.org/10.4236/ars.2013.24036
[71] Jawak, S.D. and Luis, A.J. (2014) A Semiautomatic Extraction of Antarctic Lake Features Using WorldView-2 Imagery. Photogrammetric Engineering & Remote Sensing, 80, 939-952.
http://dx.doi.org/10.14358/PERS.80.10.939
[72] Jawak, S.D. and Luis, A.J. (2013) Improved Land Cover Mapping Using High Resolution Multiangle 8-Band WorldView-2 Satellite Remote Sensing Data. Journal of Applied Remote Sensing, 7.
http://dx.doi.org/10.1117/1.JRS.7.073573
[73] Jawak, S.D., and Luis, A.J. (2015) A Rapid Extraction of Water Body Features from Antarctic Coastal Oasis Using Very High-Resolution Satellite Remote Sensing Data. ICWRCOE’ 2015, Aquatic Procedia, 4, 125-132.
http://dx.doi.org/10.1016/j.aqpro.2015.02.018
[74] Jawak, S.D., Panditrao, S.N. and Luis, A.J. (2014) Enhanced Urban Landcover Classification for Operational Change Detection Study Using Very High Resolution Remote Sensing Data. ISPRS Technical Commission VIII Symposium, Hyderabad, 9-12 December 2014, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XL-8, 773-779.
http://dx.doi.org/10.5194/isprsarchives-xl-8-773-2014
[75] Jawak, S.D., Khopkar, P.S., Jadhav, S.P. and Luis, A.J. (2013) Customization of Normalized Difference Snow Index for Extraction of Snow Cover from Cryospheric Surface Using WorldView-2 Data. Proceedings of AGSE International Conference, 16-19 December, CEPT University, Ahmedabad, AGSE, Germany, 391-398.
[76] Jawak, S.D. and Luis, A.J. (2012) WorldView-2 Satellite Remote Sensing Data for Polar Geospatial Information Mining of Larsemann Hills, East Antarctica. Proceedings of 11th Pacific (Pan) Ocean Remote Sensing Conference (PORSEC), Kochi, 05-09 November 2012, ID: PORSEC2012-24-00006.
[77] Jawak, S.D. and Luis, A.J. (2014) Spectral Bands of WorldView-2 Satellite Remote Sensing Data for Semiautomatic Land Cover Extraction in the Antarctic Environment. XXXIII SCAR and 6th Open Science Conference, Auckland, 25-28 August 2014.
[78] Jawak, S.D. and Luis, A.J. (2014) A Novel Set of Normalized Difference Snow/Ice Index Ratios for Extraction of Snow Cover from the Antarctic Environment Using Very High Resolution Satellite Data. XXXIII SCAR and 6th Open Science Conference, 25-28 August 2014, Auckland.
[79] Jawak, S.D. and Luis, A.J. (2012) Hyperspatial WorldView-2 Satellite Remote Sensing Data for Polar Geospatial Information Mining of Larsemann Hills, East Antarctica. XXXII SCAR and 5th Open Science Conference (OSC), Portland, 13-25 July 2012.
[80] Jawak, S.D. and Luis, A.J. (2011) High Resolution 8-Band WorldView-2 Satellite Remote Sensing Data for Polar Geospatial Information Mining and Thematic Elevation Mapping of Larsemann Hills, East Antarctica. 11th ISAES-2011, 10-16 July 2011, Edinburgh.
[81] Jawak, S.D. and Mathew, J. (2011) Semi-Automatic Extraction of Water Bodies and Roads from High Resolution QuickBird Satellite Data. Proceedings of Geospatial World Forum, PN-263, Hyderabad, 18-21 January 2011, 247-257.

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