TY - JOUR
T1 - The use of an artificial neural network for detecting significant changes between remotely sensed images over regions of high variability
AU - Feldberg, Idan
AU - Netanyahu, Nathan S.
AU - Shoshany, Maxim
AU - Cohen, Yafit
PY - 2001/12/1
Y1 - 2001/12/1
N2 - An Artificial Neural Network (ANN) has been developed for the task of change detection in an area of high spatio-temporal heterogeneity along a climatic gradient between humid and arid climate regions. Four recognition classes, "positive change", "negative change", "false change", and "no change" have been learned by a backpropagation ANN and then applied to Landsat images that were acquired over the study area in 1992 and 1997. A comparison with existing classification techniques indicates, in many instances, significantly improved performance due to the ANN developed.
AB - An Artificial Neural Network (ANN) has been developed for the task of change detection in an area of high spatio-temporal heterogeneity along a climatic gradient between humid and arid climate regions. Four recognition classes, "positive change", "negative change", "false change", and "no change" have been learned by a backpropagation ANN and then applied to Landsat images that were acquired over the study area in 1992 and 1997. A comparison with existing classification techniques indicates, in many instances, significantly improved performance due to the ANN developed.
UR - http://www.scopus.com/inward/record.url?scp=35574282&partnerID=8YFLogxK
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VL - 6
JO - International Geoscience and Remote Sensing Symposium (IGARSS)
JF - International Geoscience and Remote Sensing Symposium (IGARSS)
ER -