Summary
In the very near future large amounts of Remotely Sensed data will become available on a daily basis. Unfortunately, it is not clear if the processing methods are available to deal with this data in a timely fashion. This paper describes research towards an approach which will allow a user to perform a rapid pre-search of large amounts of image data for regions of interest based on texture. The method is based on a novel neural network architecture (ADAM) that is designed primarily for speed of operation by making use of computationally simple pre-processing and only uses Boolean operations in the weights of the network. To facilitate interactive use of the network, it is capable of rapid training. The paper outlines the neural network, its application to RS data in comparison with other methods, and briefly describes a fast hardware implementation of the network.
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© 1997 Springer-Verlag Berlin Heidelberg
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Austin, J. (1997). High Speed Image Segmentation Using a Binary Neural Network. In: Kanellopoulos, I., Wilkinson, G.G., Roli, F., Austin, J. (eds) Neurocomputation in Remote Sensing Data Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59041-2_23
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DOI: https://doi.org/10.1007/978-3-642-59041-2_23
Publisher Name: Springer, Berlin, Heidelberg
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