Share Email Print
cover

Proceedings Paper

Using a neural networks algorithm for high-resolution imaging in pulsed laser radar
Author(s): Mojtaba Joodaki; Guenter Kompa; Seyed M. Golam Arshad; Vahid Ahmadi; Mohammed K. Moravvej-Farshi
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

A new imaging method which can obtain the gray levels directly from the output waveform of Pulsed Laser Radar (PLR) is developed. A simple digital signal processing technique and multi layer perceptrons (MLP) type neural network (NN) have been used to obtain the gray level information from the pulse shapes. The method has been implemented in a real PLR to improve contrast and speed of 2D imaging in PLR. To compare the method with the standard method, a picture consists of 16 gray levels (from 0 for black to 1 for white) with both method has been scanned. Because of the ability of NNs in extracting the information from nonlinear and noisy data and preprocessing of the noisy input pulse shapes to the NN, the average and maximum of errors in the gray levels in comparison with standard method more than 88.5% and 72.6% improved, respectively. Because in this method the effect of the noise is decreased, it is possible to make the imaging with the same resolution as in standard method but with a lower averaging in sampling unit and this dramatically increases speed of the measurements.

Paper Details

Date Published: 13 November 2001
PDF: 10 pages
Proc. SPIE 4471, Algorithms and Systems for Optical Information Processing V, (13 November 2001); doi: 10.1117/12.449335
Show Author Affiliations
Mojtaba Joodaki, Univ. Gesamthochschule Kassel (Germany)
Guenter Kompa, Univ. Gesamthochschule Kassel (Germany)
Seyed M. Golam Arshad, Univ. Gesamthochschule Kassel (Germany)
Vahid Ahmadi, Tarbiat Modarres Univ. (Iran)
Mohammed K. Moravvej-Farshi, Tarbiat Modarres Univ. (Iran)


Published in SPIE Proceedings Vol. 4471:
Algorithms and Systems for Optical Information Processing V
Bahram Javidi; Demetri Psaltis, Editor(s)

© SPIE. Terms of Use
Back to Top