Land cover classification using MODIS–ASTER airborne simulator (MASTER) data and NDVI: a case study of the Kochang area, Korea

New sensors and new technologies for data processing provide more capabilities to map and extract information about land cover and land use. MODIS–ASTER airborne simulator (MASTER) data acquired during the PACRIM II mission were used for land cover classification in the Kochang area, Korea. Twenty-two bands covering the visible, near-infrared, and shortwave infrared wavelengths were used in the study. A minimum noise fraction (MNF) transform was used for feature extraction to select an optimum subset of data in terms of classification accuracy. The first six MNF images obtained the most accurate classification result among all MNF data combinations. Classification using five normalized difference vegetation index (NDVI) images from the visible red band and five different near-infrared bands achieved a relatively good result (overall accuracy of 78.6%) and showed additional discriminative information for land cover classification. The integration of spectral information and NDVI data using a hierarchical classification method substantially improved the classification accuracy compared to classification with spectral data alone (from 84.35% to 90.62%). The knowledge from visual inspection of classification results and analysis of confusion matrices was used to build rules used in the hierarchical classification.

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