A Modified Phong Model for Fresnel Reflection Phenomenon of Commonly Used Materials for Space Targets

Chenghao, 刘程浩 Liu; Zhi, 李智 Li; Can, 徐灿 Xu

doi:10.3788/lop54.102901

Cited by 5 publications

(1 citation statement)

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“…Because the specular component of sunlight can cause an image to be overexposed and unusable, in legible images, the spectrometer receives the diffuse component of sunlight on each surface of the target. As shown in Figure 2, commonly used materials for the space target exhibit the Fresnel effect, i.e., as the incidence angle of light on the surface increases, the specular component tends to be stronger, and the diffuse component tends to be weaker [30]. Therefore, when a surface has both sunlight and earthshine incidents at the same time, the greater the incidence angle of the sunlight, the more prominent the influence of the earthshine.…”

Section: Complex Illumination Conditions Of the Space Targetmentioning

confidence: 99%

Space Target Material Identification Based on Graph Convolutional Neural Network

Gong

Zhao

et al. 2023

Remote Sensing

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Under complex illumination conditions, the spectral data distributions of a given material appear inconsistent in the hyperspectral images of the space target, making it difficult to achieve accurate material identification using only spectral features and local spatial features. Aiming at this problem, a material identification method based on an improved graph convolutional neural network is proposed. Superpixel segmentation is conducted on the hyperspectral images to build the multiscale joint topological graph of the space target global structure. Based on this, topological graphs containing the global spatial features and spectral features of each pixel are generated, and the pixel neighborhoods containing the local spatial features and spectral features are collected to form material identification datasets that include both of these. Then, the graph convolutional neural network (GCN) and the three-dimensional convolutional neural network (3-D CNN) are combined into one model using strategies of addition, element-wise multiplication, or concatenation, and the model is trained by the datasets to fuse and learn the three features. For the simulated data and the measured data, the overall accuracy of the proposed method can be kept at 85–90%, and their kappa coefficients remain around 0.8. This proves that the proposed method can improve the material identification performance under complex illumination conditions with high accuracy and strong robustness.

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Section: Complex Illumination Conditions Of the Space Targetmentioning

confidence: 99%