By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Figueiredo, M.A. ; NASA Goddard Space Flight Center, Greenbelt, MD, USA ; Gloster, C.

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

Published in:

Neural Networks, 1998. Proceedings. Vth Brazilian Symposium on

Date of Conference:

9-11 Dec 1998