Accuracy Improvement For Remote Sensing Based Lithological Mapping By Using Randomisation And Categorical Coincidence
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Published:
Oct 4, 2020
Keywords:
Remote sensing, Randomisation, Categorical coincidence
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Abstract
Remote sensing based lithological mapping is commonly applied in the field of earth science as it requires less resource in contrast with real field work. Limitation regarding low accuracy is a challenge that should be tackled in applying remote-sensing based classification. In this paper, an attempt to improve overall accuracy of image classification using randomisation and categorical coincidence analysis was performed. It yielded final majority classification map which has higher overall accuracy compared to the overall accuracy of the population average and the overall accuracy of the map created by including all training data.
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Nugroho, R., & Roviansah, M. (2020). Accuracy Improvement For Remote Sensing Based Lithological Mapping By Using Randomisation And Categorical Coincidence. JURNAL TEKNOLOGIA, 3(1). Retrieved from https://aperti.e-journal.id/teknologia/article/view/64
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References
Asadzadeh S, de Souza Filho CR (2016) A review on spectral processing methods for geological remote sensing. International Journal of Applied Earth Observation and Geoinformation 47:69–90 . https://doi.org/10.1016/j.jag.2015.12.004
[2] Di Tommaso I, Rubinstein N (2007) Hydrothermal alteration mapping using ASTER data in the Infiernillo porphyry deposit, Argentina. Ore Geology Reviews 32:275–290 . https://doi.org/10.1016/j.oregeorev.2006.05.004
[3] Lary DJ, Alavi AH, Gandomi AH, Walker AL (2016) Machine learning in geosciences and remote sensing. Geoscience Frontiers 7:3–10 . https://doi.org/10.1016/j.gsf.2015.07.003
[4] Wester-Ebbinghaus W (2006) Aerial photography by radio controlled model helicopter. The Photogrammetric Record 10:85–92 . https://doi.org/10.1111/j.1477-9730.1980.tb00006.x
[5] Rowan LC, Mars JC, Simpson CJ (2005) Lithologic mapping of the Mordor, NT, Australia ultramafic complex by using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Remote Sensing of Environment 99:105–126 . https://doi.org/10.1016/j.rse.2004.11.021
[6] Mars JC, Rowan LC (2010) Spectral assessment of new ASTER SWIR surface reflectance data products for spectroscopic mapping of rocks and minerals. Remote Sensing of Environment 114:2011–2025 . https://doi.org/10.1016/j.rse.2010.04.008
[7] Nugroho RP, Kusumah EP (2019) Remote Predictive Geological Mapping of Arltunga Area, Alice Springs, Australia by using Robust Classification Method: Early Assessment of Image Based Geological Mapping Method for Arid Area of Indonesia. In: PROCEEDINGS JOINT CONVENTION YOGYAKARTA 2019, HAGI – IAGI – IAFMI- IATMI (JCY 2019). Yogyakarta
[8] Mwaniki MW, Moeller MS, Schellmann G (2015) A comparison of Landsat 8 (OLI) and Landsat 7 (ETM+) in mapping geology and visualising lineaments: A case study of central region Kenya. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3:897–903
[9] D’Addario GW, Chan RA (1983) Alice Springs, Northern Territory : sheet SF5314
[10] Congedo L (2013) Semi-automatic classification plugin for QGIS. Roma: Sapienza University of Rome
[11] Chavez PS (1996) Image-based atmospheric corrections-revisited and improved. Photogrammetric engineering and remote sensing 62:1025–1035
[12] Harris JR, Grunsky EC, He J, Gorodetzky D, Brown N (2012) A robust, cross-validation classification method (RCM) for improved mapping accuracy and confidence metrics. Canadian Journal of Remote Sensing 38:69–90 . https://doi.org/10.5589/m12-013
[13] Behnia P, Harris JR, Rainbird RH, Williamson MC, Sheshpari M (2012) Remote predictive mapping of bedrock geology using image classification of Landsat and SPOT data, western Minto Inlier, Victoria Island, Northwest Territories, Canada. International Journal of Remote Sensing 33:6876–6903 . https://doi.org/10.1080/01431161.2012.693219
[14] He J, Harris JR, Sawada M, Behnia P (2015) A comparison of classification algorithms using Landsat-7 and Landsat-8 data for mapping lithology in Canada’s Arctic. International Journal of Remote Sensing 36:2252–2276 . https://doi.org/10.1080/01431161.2015.1035410
[2] Di Tommaso I, Rubinstein N (2007) Hydrothermal alteration mapping using ASTER data in the Infiernillo porphyry deposit, Argentina. Ore Geology Reviews 32:275–290 . https://doi.org/10.1016/j.oregeorev.2006.05.004
[3] Lary DJ, Alavi AH, Gandomi AH, Walker AL (2016) Machine learning in geosciences and remote sensing. Geoscience Frontiers 7:3–10 . https://doi.org/10.1016/j.gsf.2015.07.003
[4] Wester-Ebbinghaus W (2006) Aerial photography by radio controlled model helicopter. The Photogrammetric Record 10:85–92 . https://doi.org/10.1111/j.1477-9730.1980.tb00006.x
[5] Rowan LC, Mars JC, Simpson CJ (2005) Lithologic mapping of the Mordor, NT, Australia ultramafic complex by using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Remote Sensing of Environment 99:105–126 . https://doi.org/10.1016/j.rse.2004.11.021
[6] Mars JC, Rowan LC (2010) Spectral assessment of new ASTER SWIR surface reflectance data products for spectroscopic mapping of rocks and minerals. Remote Sensing of Environment 114:2011–2025 . https://doi.org/10.1016/j.rse.2010.04.008
[7] Nugroho RP, Kusumah EP (2019) Remote Predictive Geological Mapping of Arltunga Area, Alice Springs, Australia by using Robust Classification Method: Early Assessment of Image Based Geological Mapping Method for Arid Area of Indonesia. In: PROCEEDINGS JOINT CONVENTION YOGYAKARTA 2019, HAGI – IAGI – IAFMI- IATMI (JCY 2019). Yogyakarta
[8] Mwaniki MW, Moeller MS, Schellmann G (2015) A comparison of Landsat 8 (OLI) and Landsat 7 (ETM+) in mapping geology and visualising lineaments: A case study of central region Kenya. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3:897–903
[9] D’Addario GW, Chan RA (1983) Alice Springs, Northern Territory : sheet SF5314
[10] Congedo L (2013) Semi-automatic classification plugin for QGIS. Roma: Sapienza University of Rome
[11] Chavez PS (1996) Image-based atmospheric corrections-revisited and improved. Photogrammetric engineering and remote sensing 62:1025–1035
[12] Harris JR, Grunsky EC, He J, Gorodetzky D, Brown N (2012) A robust, cross-validation classification method (RCM) for improved mapping accuracy and confidence metrics. Canadian Journal of Remote Sensing 38:69–90 . https://doi.org/10.5589/m12-013
[13] Behnia P, Harris JR, Rainbird RH, Williamson MC, Sheshpari M (2012) Remote predictive mapping of bedrock geology using image classification of Landsat and SPOT data, western Minto Inlier, Victoria Island, Northwest Territories, Canada. International Journal of Remote Sensing 33:6876–6903 . https://doi.org/10.1080/01431161.2012.693219
[14] He J, Harris JR, Sawada M, Behnia P (2015) A comparison of classification algorithms using Landsat-7 and Landsat-8 data for mapping lithology in Canada’s Arctic. International Journal of Remote Sensing 36:2252–2276 . https://doi.org/10.1080/01431161.2015.1035410