知识图谱嵌入的光谱解混算法  

Knowledge graph embedding spectral unmixing

作　　者：吴瑞 罗文斐[1] 陈江浩 WU Rui;LUO Wenfei;CHEN Jianghao(School of Geography,South China Normal University,Guangzhou 510631,China)

机构地区：[1]华南师范大学地理科学学院,广州510631

出　　处：《遥感学报》2024年第8期2073-2088,共16页NATIONAL REMOTE SENSING BULLETIN

基　　金：高分辨率对地观测系统重大专项(编号:11-Y20A40-9002-15/17)。

摘　　要：在光谱解混过程中,在端元集合中选择有效的端元子集进行解混是至关重要的,但端元子集选择会受到端元光谱变异性的影响,导致选择的结果以及解混精度具有一定的不确定性。本文提出了一种知识图谱嵌入的光谱解混算法KGESU (Knowledge Graph Embedding Spectral Unmixing),在利用光谱特征进行解混的同时,引入一定的先验知识来进一步提高端元选择的可靠性,从而提升解混的精度。主要涉及两个核心问题:一是地学知识图谱的嵌入,二是引入先验的光谱解混。前者借助了TransE模型来实现对地学知识图谱的图嵌入训练;后者则在知识图谱嵌入的基础上进行知识推理,并把推理结果融入到光谱解混的过程,从而实现了一个引入先验知识的光谱解混算法。为了验证算法的有效性,通过高分五号高光谱相机所采集的真实图像进行实验,与其他经典算法作对比,结果表明本文的算法具有更好的解混效果,证明了在光谱解混过程中使用一定的地学先验知识有助于提升解混精度,KGESU算法具有一定的应用前景。Selecting effective endmembers from a set of endmembers is important in the process of spectral unmixing. However, the selection of endmembers will be affected by the spectral variability of endmembers, which results in a certain uncertainty in the results of selection and the accuracy of unmixing. This study combines geoscience prior knowledge with sparse unmixing to solve this problem, and a Knowledge Graph Embedding Spectral Unmixing(KGESU) algorithm is proposed. While utilizing spectral features, certain prior knowledge is introduced to further improve the reliability of endmember selection.The implementation steps of the KGESU algorithm involve two issues the embedding training of geoscience knowledge graph and spectral unmixing with priori knowledge. The embedding training of geoscience knowledge graph transforms geoscience knowledge into a structured expression form through knowledge graph. Then, the TransE model is used for graph embedding. We perform knowledge reasoning according to the knowledge graph embedding to address the second issue. Then, a reasoning – weighting sparse unmixing algorithm is developed to integrate the process of reasoning and unmixing.Experiments are conducted to validate the effectiveness of the proposed method. The prior knowledge is instantiated with the aid of auxiliary data such as Landsat 8 and GDEMV2. The spectral unmixing data are GF-5 satellite data. The GF-2 data with a resolution of 1 m after graphic fusion are used for verification. Compared with the traditional pixel-by-pixel evaluation, this study expands the evaluation window. The sensitivity of different resolution images to registration errors is reduced by increasing the overlap area between pixels and allocating the residuals. The root mean square error of each endmember, the mean of the root mean square error of each endmember, and the overall root mean square error of the image are used as evaluation indexes to evaluate the unmixing results. Results demonstrate that the KGESU algorithm outperforms the state-

关 键 词：遥感 知识图谱 知识图谱嵌入 端元子集选择 光谱解混 

分 类 号：P2[天文地球—测绘科学与技术]