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Remote Sensing Image Analysis via a Texture Classification Neural Network

Greenspan, Hayit K. and Goodman, Rodney (1993) Remote Sensing Image Analysis via a Texture Classification Neural Network. In: Advances in Neural Information Processing Systems 5 (NIPS 1992). Advances in Neural Information Processing Systems. No.5. Morgan Kaufmann , San Mateo, CA, pp. 425-432. ISBN 1-55860-274-7.

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In this work we apply a texture classification network to remote sensing image analysis. The goal is to extract the characteristics of the area depicted in the input image, thus achieving a segmented map of the region. We have recently proposed a combined neural network and rule-based framework for texture recognition. The framework uses unsupervised and supervised learning, and provides probability estimates for the output classes. We describe the texture classification network and extend it to demonstrate its application to the Landsat and Aerial image analysis domain.

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Additional Information:© 1993 Morgan Kaufmann. This work is supported in part by Pacific Bell, and in part by DARPA and ONR under grant no. N00014-92-J-1860. H. Greenspan is supported in part by an Intel fellowship. The research described in this paper was carried out in part by the Jet Propulsion Laboratories, California Institute of Technology. We would like to thank Dr. C. Anderson for his pyramid software support and Dr. L. Matthies for the autonomous vehicle images.
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Office of Naval Research (ONR)N00014-92-J-1860
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Series Name:Advances in Neural Information Processing Systems
Issue or Number:5
Record Number:CaltechAUTHORS:20160202-165824779
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:64177
Deposited By: Kristin Buxton
Deposited On:03 Feb 2016 23:26
Last Modified:03 Oct 2019 09:35

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