USE OF MULTITEMPORAL INFORMATION TO IMPROVE CLASSIFICATION PERFORMANCE OF TM SCENES IN COMPLEX TERRAIN

The discrimination of land cover types by means of satellite remotely sensed data is a very challenging task in extremely complex and heterogeneous environments where the surfaces are hardly spectrally identifiable. In these cases the use of multitemporal acquisitions could be expected to enhance substantially classification performance with respect to single scenes, when inserted in procedures which exploit all the information available. The present work discusses this hypothesis and employs three TM scenes of gently undulated terrain in Tuscany (central Italy) from different seasons of one year (February, May and August). The three phenological stages of the vegetated surfaces provided additional statistical information with respect to single scenes. Classification was tested with gaussian maximum likelihood classifiers, both separately on each of the three TM passages and, suitably adapted, on the whole multitemporal set. An iterative process using probabilities estimated from the error matrices of previous single image classifications was also tested. Results of tests show that multitemporal information greatly improves classification performance, particularly when using the statistical procedure described.