Mining Land Cover Information Using Multilayer Perceptron and Decision Tree from MODIS Data
Journal of the Indian Society of Remote Sensing
Land cover (LC) changes play a major role in global as well as at regional scale patterns of the climate and biogeochemistry of the Earth system. LC information presents critical insights in understanding of Earth surface phenomena, particularly useful when obtained synoptically from remote sensing data. However, for developing countries and those with large geographical extent, regular LC mapping is prohibitive with data from commercial sensors (high cost factor) of limited spatial coverage
... spatial coverage (low temporal resolution and band swath). In this context, free MODIS data with good spectrotemporal resolution meet the purpose. LC mapping from these data has continuously evolved with advances in classification algorithms. This paper presents a comparative study of two robust data mining techniques, the multilayer perceptron (MLP) and decision tree (DT) on different products of MODIS data corresponding to Kolar district, Karnataka, India. The MODIS classified images when compared at three different spatial scales (at district level, taluk level and pixel level) shows that MLP based classification on minimum noise fraction components on MODIS 36 bands provide the most accurate LC mapping with 86% accuracy, while DT on MODIS 36 bands principal components leads to less accurate classification (69%). Keywords Land cover . Multilayer perceptrons . Decision tree . Principal component analysis . Minimum noise fraction Introduction Land cover (LC) changes induced by human and natural processes are linked to climate and weather in many complex ways. These linkages between LC dynamics and climate include the exchange of greenhouse gases (water vapor, carbon dioxide, methane, etc.) between the land surface and the atmosphere, the radiation balance of the land surface, the exchange of sensible heat in the atmosphere, and the roughness of the land surface.