אסיף מאגר המחקר החקלאי

The use of an artificial neural network for detecting significant changes between remotely sensed images over regions of high variability

Year:

2001

Source of publication :

Authors :

Volume :

6

Co-Authors:

Feldberg, I., Dept. of Mathematics and Comp. Sci., Bar-Ilan University, Ramat-Gan 52900, Israel
Netanyahu, N.S., Dept. of Mathematics and Comp. Sci., Bar-Ilan University, Ramat-Gan 52900, Israel
Shoshany, M., Dept. of Mathematics and Comp. Sci., Bar-Ilan University, Ramat-Gan 52900, Israel
Cohen, Y., Dept. of Mathematics and Comp. Sci., Bar-Ilan University, Ramat-Gan 52900, Israel

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Abstract:

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.

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