A bidirectional reflectance distribution function model of space targets in visible spectrum based on GA-BP network
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A bidirectional reflectance distribution function model of space targets in visible spectrum based on GA‑BP network Yuying Liu1 · Jingjing Dai1 · Sisi Zhao2 · Jinghao Zhang2 · Tong Li2 · Weidong Shang2 · Yongchao Zheng2 · Zhiyong Wang1 Received: 27 November 2019 / Accepted: 27 April 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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.
1 Introduction BRDF is used to describe how irradiance in a given incident direction affects radiance in a given direction of reflection [1], it has a wide utilization in graphic rendering [2, 3], remote sensing [4, 5], target recognition [6], and other fields [7, 8]. In general, BRDF model can be divided into three categories: theoretical, empirical, and experimental model [9]. An empirical BRDF model named five-parameter model was proposed by Wu [10]. Wu used genetic algorithm to fit the undetermined parameters, the results demonstrate that the model has good agreement with experimental BRDF data, and it is a good choice to analyze reflectance properties of materials [11]. However, each of the different models is only suitable for typical material. To construct a model with wider applicability, we proposed a data-driven model based on GA-BP network.
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00340-020-07455-y) contains supplementary material, which is available to authorized users. * Zhiyong Wang [email protected] 1
Institute of Laser Engineering, Beijing University of Technology, Beijing 100020, China
Beijing Institute of Space Mechanics and Electricity, Beijing 100094, China
2
The Back-Propagation neural network (BP Network) is the most widely used network (about 80%), which embodies the essence of neural network [12]. BP network can judge the error of output value and actual value according to the error formula, and it can modify the connection weight and threshold repeatedly to make the network error reach the expected value, so it has a strong function approximation ability. As long as the reasonable network structure and parameters are chosen, it can approximate any continuous function [13], so BP is a good choice to build BRDF model. However, BP network has its own disadvantages; because of the principle of gradient descent to adjust the weights, the training results can easily fall into local optimum and cannot achieve the
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