Spatio-temporal contextual classification of remotely sensed multispectral data

A spatio-temporal contextual classifier that can utilize both spatial and temporal information is investigated. Experiments carried out with Landsat TM data are reported. They show that spatial correlation contexts are more useful than the other contexts. The use of the homogeneity test followed by a selective application of the contextual rule is more effective than the totally recursive case in the sense of both classification accuracy and computation. Classification performance is compared with that of the maximum-likelihood classifier and the ECHO (extraction and classification of homogeneous objects) classifier.>