Computational spectral imaging technology with high light utilization for space target detection

Li, Yanli; Jin, Libing; Zhao, Haibo; Zhang, Xiaoming; Xue, Fu‐Shan; Wang, Yechao

doi:10.1117/12.2623797

Cited by 1 publication

(2 citation statements)

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“…Early spectroscopic measurements of the space targets were made through groundbased telescopes [2,[4][5][6], and the data obtained in this way were a single spectrum, so the material identification methods can only rely on spectral features. At present, researchers are gradually transitioning from ground-based detection to space-based detection [3,23,24], and the close detection distance in space-based detection enables hyperspectral images to show relatively rich spatial features. Existing material identification methods using spatial features are usually designed under ideal imaging scenarios, in which the detector and space target are relatively stationary during the measurement of hyperspectral images (e.g., the detector is executing a hovering operation [25] on the space target with three-axis stabilization).…”

Section: Complex Illumination Conditions Of the Space Targetmentioning

confidence: 99%

“…Space target recognition is of great significance in the protection of the space environment as well as in the safe and sustainable development and utilization of space resources [1]. In a variety of space observation methods, spectra contain a large amount of information in the wavelength dimension, and they can provide a reliable basis for the identification of the space target surface materials, thereby meeting the needs for space security and determining space target behavior intention accurately [2,3]. Therefore, the development of spectra-based space target material identification research is an important topic in space target recognition.…”

Section: Introductionmentioning

confidence: 99%

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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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