Comparison of multi-temporal image classification methods
Dony Kushardono, K. Eukue, Haruhisa Shimoda, Toshibumi Sakata · 2002
One of the promising methods which can be thought to increase classification accuracies in remote sensing is the use of multi-temporal images. The authors propose multi-temporal image classification methods using backpropagation networks and fuzzy neural networks as classifiers and two kinds of classification models based on co-occurrence matrix as spatial information source. They are compared with conventional methods such as the likelihood addition method, the likelihood majority method and the Dempster-Shafer rule method.