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

Idan Feldberg; Nathan S. Netanyahu; Maxim Shoshany; Yafit Cohen

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

Idan Feldberg, Nathan S. Netanyahu, Maxim Shoshany, Yafit Cohen

Department of Mathematics

Bar-Ilan University

Research output: Contribution to conference › Paper › peer-review

2 Scopus citations

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.

Original language	English
Pages	2704-2706
Number of pages	3
State	Published - 2001
Event	2001 International Geoscience and Remote Sensing Symposium (Igarrs 2001) - Sydney, NSW, Australia Duration: 9 Jul 2001 → 13 Jul 2001

Conference

Conference	2001 International Geoscience and Remote Sensing Symposium (Igarrs 2001)
Country/Territory	Australia
City	Sydney, NSW
Period	9/07/01 → 13/07/01

Cite this

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

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author = "Idan Feldberg and Netanyahu, {Nathan S.} and Maxim Shoshany and Yafit Cohen",

year = "2001",

language = "אנגלית",

pages = "2704--2706",

note = "2001 International Geoscience and Remote Sensing Symposium (Igarrs 2001) ; Conference date: 09-07-2001 Through 13-07-2001",

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The use of an artificial neural network for detecting significant changes between remotely sensed images over regions of high variability. / Feldberg, Idan; Netanyahu, Nathan S.; Shoshany, Maxim et al.
2001. 2704-2706 Paper presented at 2001 International Geoscience and Remote Sensing Symposium (Igarrs 2001), Sydney, NSW, Australia.

Research output: Contribution to conference › Paper › peer-review

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AU - Netanyahu, Nathan S.

AU - Shoshany, Maxim

AU - Cohen, Yafit

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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.

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T2 - 2001 International Geoscience and Remote Sensing Symposium (Igarrs 2001)

Y2 - 9 July 2001 through 13 July 2001

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

Abstract

Conference

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