DocumentCode :
757509
Title :
Remote sensing of forest change using artificial neural networks
Author :
Gopal, Sucharita ; Woodcock, Curtis
Author_Institution :
Dept. of Geogr., Boston Univ., MA, USA
Volume :
34
Issue :
2
fYear :
1996
fDate :
3/1/1996 12:00:00 AM
Firstpage :
398
Lastpage :
404
Abstract :
A prolonged drought in the Lake Tahoe Basin in California has resulted in extensive conifer mortality. This phenomenon can be analyzed using (multitemporal) remote sensing data. Prior research in the same region used more traditional methods of change detection. The present paper introduces a third approach to change detection in remote sensing based on artificial neural networks. The neural network architecture used is a multilayer feedforward network. The results of the study indicate that the artificial neural network (ANN) estimates conifer mortality more accurately than the other approaches. Further, an analysis of its architecture reveals that it uses identifiable scene characteristics-the same as those used by a Gramm-Schmidt transformation. ANN models offer a viable alternative for change detection in remote sensing
Keywords :
feedforward neural nets; forestry; geophysical signal processing; geophysical techniques; geophysics computing; image processing; image sequences; remote sensing; ANN model; California; Lake Tahoe Basin; USA; United States; artificial neural networks; change detection; conifer mortality; drought; forest change; forest damage; forestry; geophysical measurement technique; image processing; image sequences; multilayer feedforward network; multitemporal remote sensing; neural net; optical imaging; pattern recognition; vegetation mapping; Artificial neural networks; Associate members; Image analysis; Image segmentation; Lakes; Multi-layer neural network; Neural networks; Remote sensing; Technological innovation; Vegetation mapping;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/36.485117
Filename :
485117
Link To Document :
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