Implementation of a probabilistic neural network for multi-spectral image classification on an FPGA based custom computing machine

As the demand for higher performance computers for the processing of remote sensing science algorithms increases, the need to investigate new computing paradigms is justified. Field programmable gate arrays (FPGA) enable the implementation of algorithms at the hardware gate level, leading to orders...

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Bibliographic Details
Main Authors: Figueiredo, M.A., Gloster, C.
Format: Conference Proceeding
Language:English
Subjects:
Computer applications
Field programmable gate arrays
Hardware
High performance computing
Hyperspectral sensors
Instruments
Microprocessors
Multispectral imaging
Neural networks
Remote sensing
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Summary:As the demand for higher performance computers for the processing of remote sensing science algorithms increases, the need to investigate new computing paradigms is justified. Field programmable gate arrays (FPGA) enable the implementation of algorithms at the hardware gate level, leading to orders of magnitude performance increase over microprocessor based systems. The automatic classification of space borne multispectral images is an example of a computation intensive application that only tends to increase as instruments start to explore hyperspectral capabilities. A probabilistic neural network is used here to classify pixels of a multispectral LANDSAT-2 image. The implementation described utilizes a commercial-off-the-shelf FPGA based custom computing machine.
DOI:10.1109/SBRN.1998.731021