A remotely sensed data separation method with neural networks

IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217) ◽

10.1109/igarss.2001.978335 ◽

2002 ◽

Author(s):

T. Yoshida ◽

S. Omatu

Keyword(s):

Neural Networks ◽

Remotely Sensed ◽

Separation Method ◽

Remotely Sensed Data ◽

Data Separation

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Thematic mapping from remotely sensed data with neural networks: MLP, RBF and PNN based approaches

Journal of Geographical Systems ◽

10.1007/pl00011477 ◽

2001 ◽

Vol 3 (3) ◽

pp. 217-232 ◽

Author(s):

Giles M. Foody

Keyword(s):

Neural Networks ◽

Remotely Sensed ◽

Remotely Sensed Data ◽

Thematic Mapping

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Remotely sensed data analysis using two neural networks and its application to land cover mapping

IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174) ◽

10.1109/igarss.1998.702920 ◽

1998 ◽

Author(s):

H. Murai ◽

S. Omatu ◽

S. Oe

Keyword(s):

Neural Networks ◽

Data Analysis ◽

Remotely Sensed ◽

Land Cover Mapping ◽

Remotely Sensed Data

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Uncertainty in Land Cover Mapping from Remotely Sensed Data Using Textural Algorithm and Artificial Neural Networks

Uncertainty in Remote Sensing and GIS ◽

10.1002/0470035269.ch7 ◽

2006 ◽

pp. 99-118 ◽

Author(s):

Anna M. Jakomulska ◽

Jan P. Radomski

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Remotely Sensed ◽

Land Cover Mapping ◽

Remotely Sensed Data ◽

Artificial Neural

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The application of artificial neural networks to the analysis of remotely sensed data

International Journal of Remote Sensing ◽

10.1080/01431160701352154 ◽

2007 ◽

Vol 29 (3) ◽

pp. 617-663 ◽

Author(s):

J. F. Mas ◽

J. J. Flores

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Remotely Sensed ◽

Remotely Sensed Data ◽

Artificial Neural

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Mapping the species richness and composition of tropical forests from remotely sensed data with neural networks

Ecological Modelling ◽

10.1016/j.ecolmodel.2005.11.007 ◽

2006 ◽

Vol 195 (1-2) ◽

pp. 37-42 ◽

Author(s):

Giles M. Foody ◽

Mark E.J. Cutler

Keyword(s):

Neural Networks ◽

Species Richness ◽

Tropical Forests ◽

Remotely Sensed ◽

Remotely Sensed Data

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Using artificial neural networks for synthetic surface fitting and the classification of remotely sensed data

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/s0303-2434(99)85031-4 ◽

1999 ◽

Vol 1 (1) ◽

pp. 78-84 ◽

Author(s):

Kamini K Mohanty ◽

Tapan J Majumdar

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Remotely Sensed ◽

Surface Fitting ◽

Remotely Sensed Data ◽

Artificial Neural

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Parallel genetic algorithm for the design of neural networks: an application to the classification of remotely sensed data

10.1117/12.326727 ◽

1998 ◽

Author(s):

Sebastiano Stramaglia ◽

Giuseppe Satalino ◽

A. Sternieri ◽

P. Anelli ◽

Palma N. Blonda ◽

...

Keyword(s):

Genetic Algorithm ◽

Neural Networks ◽

Remotely Sensed ◽

Parallel Genetic Algorithm ◽

Remotely Sensed Data

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A New Spatio-Temporal Fusion Method for Remotely Sensed Data Based on Convolutional Neural Networks

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2019.8898524 ◽

2019 ◽

Author(s):

Yunfei Li ◽

Chenying Liu ◽

Lin Yan ◽

Jun Li ◽

Antonio Plaza ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Remotely Sensed ◽

Fusion Method ◽

Remotely Sensed Data ◽

Spatio Temporal

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Study on the characteristics of the supervised classification of remotely sensed data using artificial neural networks

IGARSS'97. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing - A Scientific Vision for Sustainable Development ◽

10.1109/igarss.1997.615933 ◽

2002 ◽

Author(s):

K.N. Paek ◽

Y.S. Song ◽

H.S. Chae ◽

K.E. Kim

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Supervised Classification ◽

Remotely Sensed ◽

Remotely Sensed Data ◽

Artificial Neural

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A comparative study of radial basis function neural networks and wavelet neural networks in classification of remotely sensed data

Proceedings of the 5th Biannual World Automation Congress ◽

10.1109/wac.2002.1049584 ◽

2003 ◽

Author(s):

Chih-Cheng Hung ◽

Youngsup Kim ◽

T.L. Coleman

Keyword(s):

Neural Networks ◽

Comparative Study ◽

Radial Basis Function ◽

Basis Function ◽

Remotely Sensed ◽

Wavelet Neural Networks ◽

Remotely Sensed Data ◽

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