GLOBAL LAND COVER CLASSIFICATION USING MODIS SURFACE REFLECTANCE PRODUCTS

The objective of this study is to develop high accuracy land cover classification algorithm for Global scale by using multi-temporal MODIS land reflectance products. In this study, time-domain co-occurrence matrix was introduced as a classification feature which provides time-series signature of land covers. Further, the non-parametric minimum distance classifier was introduced for time-domain co-occurrence matrix, which performs multi-dimensional pattern matching for time-domain co-occurrence matrices of a classification target pixel and each classification classes. The global land cover classification experiments have been conducted by applying the proposed classification method using 46 multi-temporal(in one year) SR(Surface Reflectance 8-Day L3) and NBAR(Nadir BRDF-Adjusted Reflectance 16-Day L3) products, respectively. IGBP 17 land cover categories were used in our classification experiments. As the results, SR product and NBAR product showed similar classification accuracy of 99%.