Abstract
Multispectral remote sensing images have been widely used for automated land use
and land cover classification tasks. Often thematic classification is done using single date image,
however in many instances a single date image is not informative enough to distinguish between
different land cover types. In this paper we show how one can use multiple images, collected at
different times of year (for example, during crop growing season), to learn a better classifier. We
propose two approaches, an ensemble of classifiers approach and a co-training based approach, and
show how both of these methods outperform a straightforward stacked vector approach often used
in multi-temporal image classification. Additionally, the co-training based method addresses the
challenge of limited labeled training data in supervised classification, as this classification scheme
utilizes a large number of unlabeled samples (which comes for free) in conjunction with a small
set of labeled training data.