Approximate Search on Massive Spatiotemporal Datasets
Remote sensing data sets frequently suffer from noise due to atmospheric conditions and instrument issues. This noise negatively affects the usability of these data sets and therefore noise reduction techniques are frequently used to reduce the impact of noise. A well-known remote sensing data set, MODIS Enhance Vegetation Index (EVI), measures the amount of vegetation (based on surface reflectance) observed from satellite. This data set has been used for land cover change detection, in both regional-scale and global-scale studies. Many noise reduction techniques have seen proposed in the remote sensing literature but comparative studies to understand relative performance of these techniques are scarce. Furthermore, the existing comparative studies typically evaluate a small number of techniques on a specific geographical region. Therefore, little is known about the global applicability of these techniques. In addition, time series based land cover change detection algorithms are known to be negatively impacted by the presence of noise. This paper investigates the interrelations of regional noise characteristics, change detection algorithms, and noise reduction methods. The methods for noise reduction are applied in three different geographic regions and through comparison we outline the noise characteristics relevant to the performance of land cover change detection.