Modeling and Verification of Space-Based Optical Scattering Characteristics of Space Objects

Chengming, 孙成明 Sun; Yan, 袁艳 Yuan; Qunbo, 吕群波 Lü

doi:10.3788/aos201939.1129001

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, with the development of research on the influencing factors of material identification [15][16][17][18], it has been found that the applicability domain of methods using only spectral features may be narrow. These methods require consistent spectral features of the same material, but, because the space target materials are non-Lambertian objects, the spectra of the same material can become different when the target is exposed to complex illumination conditions consisting of sunlight and earthshine [19]. Many researchers have experimentally proved this phenomenon and discussed its properties.…”

Section: Introductionmentioning

confidence: 99%

Space Target Material Identification Based on Graph Convolutional Neural Network

Gong

Zhao

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

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.

show abstract