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Land-cover Classification of Imagery from Landsat Operational Land Imager Based on Optimum Index Factor
Tri Dev Acharya, In Tae Yang, and Dong Ha Lee
(Received April 10, 2017; Accepted January 30, 2018)
Keywords: land-cover classification, SAM, SVM, Landsat 8, OLI, OIF, Korea
With over four decades spent collecting spaceborne moderate resolution imagery, Landsat represents the longest remote sensing mission in the world, and has had various applications. Land-cover mapping is its heritage for research around the world. Landsat 8 continues the legacy of previous Landsat systems, with a new Operational Land Imager (OLI) sensor that has high spectral resolution and improved signal-to-noise ratio for better characterization of land-cover. With improved quality, data size also increases. Hence, with limited research in adjusting data size, it is necessary to explore robust land-cover classification techniques that produce accurate maps with more or fewer inputs. Optimum Index Factor (OIF) is a statistic value that can be used to select the optimum combination of three-band in a satellite image that has the highest amount of information. In this study, we explore the land-cover classification of OLI imagery based on OIF. Two test sites were selected around the hilly regions of Korea for OLI original, first-rank OIF composite and OLI original with sum derivative of top-three OIF ranked composites. These three composites were classified with the well-known Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classifiers. The results were then analyzed and compared based on producer accuracy, user accuracy, overall accuracy, and kappa coefficient. The result shows that the first-ranked OIF with three-band composite shows similar classification accuracy in SVM and slightly less in SAM, while the ten band composite with OLI original bands and sum derivative of top-three OIF rank shows the same result or a small improvement in SVM classification. OIF-derivative composites can be useful in classification problems depending on the case, depending on whether minimum data for a similar result or derivative data for higher accuracy is preferred.
Corresponding author: Dong Ha Lee
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