Evaluation of distance measure methods for vegetation index time-series data
植被指数时序数据距离测度方法评价

In order to evaluate the clustering accuracy of different distance measure methods for vegetation index time-series data, we make a comprehensive comparison among six distance measure methods (Euclidean distance, spectral information divergence, spectral angle cosine, kernel spectral angle cosine, correlation coeffi cient and spectral angle cosine-Euclidean distance) based on the MODIS Enhanced Vegetation Index (EVI) time-series data in China by selecting 468 test pixels across 55 vegetation types and a test region. The test results indicate that the correlation coeffi cient method shows the lowest clustering accuracy. However, the spectral angle cosine-Euclidean distance method which captures both the curve shape and the amplitude features of the vegetation index time-series data shows the highest clustering accuracy among the six methods. Both the Euclidean distance method which is only sensitive to the spectral brightness and the spectral angle cosine method which is only sensitive to the curve shape perform an inferior clustering accuracy not only in distinguishing different land cover types but also in the regional application. Although the kernel spectral angle cosine method does not show high clustering accuracy in the test at the point level, it shows better performance in the regional application. The spectral information divergence method has a modest performance in the test both at the point level and at the regional level.