A bidirectional reflectance distribution function model of space targets in visible spectrum based on GA-BP network
Creators
- 1. Beijing University of Technology. Institute of Laser Engineering (China)
- 2. Beijing Institute of Space Mechanics and Electricity (China)
Description
An optimized Back-Propagation network (BP network) based on Genetic Algorithm (GA) was introduced to construct bidirectional reflectance distribution function (BRDF) model. To verify the performance of GA-BP network, two different kinds of space target materials were used for experiment. Based on the experimental data, we used GA to simulate the undetermined parameters of a five-parameter BRDF model, and used GA-BP network and BP network to construct a new BRDF model respectively. The fitting results manifest that the GA-BP network is suitable for construct a new BRDF model and outperforms the five-parameter BRDF model in speed and accuracy under the same condition.
Additional details
Identifiers
Publishing Information
- Journal Title
- Applied Physics. B, Lasers and Optics
- Journal Volume
- 126
- Journal Issue
- 6
- Journal Page Range
- vp.
- ISSN
- 0946-2171
- CODEN
- APBOEM
INIS
- Country of Publication
- Germany
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 55058419
- Subject category
- S97: MATHEMATICAL METHODS AND COMPUTING; S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS;
- Descriptors DEI
- ACCURACY; ADAPTIVE SYSTEMS; ALGORITHMS; BORON PHOSPHIDES; DATA-FLOW PROCESSING; DISTRIBUTION FUNCTIONS; GENETIC ALGORITHMS; LOCAL AREA NETWORKS; NETWORK ANALYSIS; PERFORMANCE; REFLECTION; SPACE; VELOCITY; VISIBLE RADIATION
- Descriptors DEC
- ALGORITHMS; BORON COMPOUNDS; COMPUTER NETWORKS; COMPUTERIZED CONTROL SYSTEMS; CONTROL SYSTEMS; ELECTROMAGNETIC RADIATION; FUNCTIONS; MATHEMATICAL LOGIC; ON-LINE CONTROL SYSTEMS; ON-LINE SYSTEMS; PHOSPHIDES; PHOSPHORUS COMPOUNDS; PNICTIDES; PROGRAMMING; RADIATIONS
Optional Information
- Copyright
- Copyright (c) 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020