Use of Intra-annual Satellite Imagery Time-series for Land Cover Characterisation Purpose

Automatic image classification often fails at separating a large number of land cover classes that punctually may present similar spectral reflectances. To improve the classification accuracy in such situations, multi-temporal satellite data has proven to be valuable auxiliary information. In this paper, we present a study exploring the usefulness of intra-annual satellite images timeseries for automatic land cover classification. The reported work aims at producing a land cover classification of continental Portugal from multi-spectral and multi-temporal MODIS satellite images acquired at a spatial resolution of 500 metres for the year 2000. We started our study by performing a single date classification to define the month with the best score as a benchmark to compare with classification accuracies obtained with sets of images from various dates. Then, we considered various combinations of twelve intra-annual image observations (one per month) to quantify the gain when integrating temporal information in the classification process. Curiously, the results we obtained show that multi-temporal information does not significantly improve overall classification accuracy, but in particular it permits to better separate similar land cover classes even if those remain wrongly identified. Surprisingly also, we show that only few (typically 2) dates are sufficient to reach optimal performance of our multi-temporal classifier. In our study we used a Support Vector Machine learning approach.