To fuse hyperspectral and Light Detection And Ranging (LiDAR), we propose a semisupervised graph fusion (SSGF) approach. We apply morphological filters to LiDAR and the first few components of hyperspectral data to model the height and spatial information, respectively. Then, the proposed SSGF is used to project the spectral, elevation, and spatial features onto a lower subspace to obtain the new features. In particular, the objective of SSGF is to maximize the class separation ability and preserve the local neighborhood structure by using both labeled and unlabeled samples. Experimental results on the hyperspectral and LiDAR data from the 2013 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest demonstrated the superiority of the SSGF.

中文翻译：

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半监督图融合的高光谱和 LiDAR 分类

为了融合高光谱和光检测与测距 (LiDAR)，我们提出了一种半监督图融合 (SSGF) 方法。我们将形态滤波器应用于 LiDAR 和高光谱数据的前几个组件，分别对高度和空间信息进行建模。然后，提出的 SSGF 用于将光谱、高程和空间特征投影到较低的子空间以获得新的特征。特别是，SSGF 的目标是通过使用标记和未标记样本来最大化类分离能力并保留局部邻域结构。2013 年 IEEE 地球科学与遥感学会 (GRSS) 数据融合竞赛的高光谱和 LiDAR 数据的实验结果证明了 SSGF 的优越性。