Space object material identification method based on Tucker decomposition is proposed and demonstrated experimentally. Space target generally has a low spatial resolution because of the limitation in detection distance. Hyperspectral imaging (HSI) technology can capture the image from visible light to shortwave infrared continuously while providing the spatial and spectral information of the target, thereby introducing a new space target detection and identification approach. However, the data obtained by the HSI system often contain mixed pixels, thereby causing difficulties in target material identification. In this work, a material identification method of hyperspectral data based on Tucker decomposition is proposed by combining mixed spectral theory with tensor decomposition. The feasibility of the method is verified by using satellite model hyperspectral data with different spatial resolutions compared with the endmember obtained from the non-negative matrix decomposition (NMF), independent component analysis (ICA), tensor singular value decomposition (t-SVD). The average CORR of NMF, ICA, t-SVD and the proposed method is 0.2694, 0.5818, 0.6397 and 0.937, correspondingly. Therefore, the proposed method has demonstrated a more remarkable performance in terms of material identification, the analysis results of the material abundance distribution that used the proposed method.

提出了基于塔克分解的空间物体材料识别方法，并进行了实验验证。由于检测距离的限制，空间目标通常具有较低的空间分辨率。高光谱成像（HSI）技术可以连续捕获从可见光到短波红外的图像，同时提供目标的空间和光谱信息，从而引入了一种新的空间目标检测和识别方法。然而，由HSI系统获得的数据通常包含混合像素，从而导致目标材料识别上的困难。结合混合光谱理论和张量分解，提出了一种基于塔克分解的高光谱数据材料识别方法。与使用非负矩阵分解（NMF），独立分量分析（ICA），张量奇异值分解（t-SVD）获得的最终成员相比，使用具有不同空间分辨率的卫星模型高光谱数据验证了该方法的可行性。NMF，ICA，t-SVD和所提出的方法的平均CORR分别为0.2694、0.5818、0.6397和0.937。因此，该方法在材料识别，使用该方法的材料丰度分布的分析结果方面表现出了更加出色的性能。ICA，t-SVD和建议的方法分别为0.2694、0.5818、0.6397和0.937。因此，该方法在材料识别，使用该方法的材料丰度分布的分析结果方面表现出了更加出色的性能。ICA，t-SVD和建议的方法分别为0.2694、0.5818、0.6397和0.937。因此，该方法在材料识别，使用该方法的材料丰度分布的分析结果方面表现出了更加出色的性能。