Discriminative Graph-Based Fusion of HSI and LiDAR Data for Urban Area Classification
Discriminative Graph-Based Fusion of HSI and LiDAR Data for Urban Area Classification
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DOI：
10.1109/lgrs.2017.2687519
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发表时间：
2017-04
影响因子：
4.8
通讯作者：
Yanfeng Gu;Qingwang Wang
中科院分区：
文献类型：
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作者：
Yanfeng Gu;Qingwang Wang
提出了一种新的基于判别图的融合（DGF）方法，用于城市区域分类，融合来自两个数据源的异质特征，高光谱图像（HSI）和光探测和测距（LiDAR）数据。这些特征包括HSI中的光谱特征，LiDAR数据中的高度，以及图像处理技术中的几何形状，如形态学轮廓（MP）。我们提出的DGF方法耦合降维和异构特征融合。该方法的核心思想是通过最小化保持每个类的局部几何的相似项和最大化包含类间距离关系的相异项来搜索投影矩阵。因此，所提出的方法可以拉近在一起的同一类的样本，而推动那些不同的类除了在投影空间融合图构造的不同群体的异构功能。图的边由核度量。此外，多尺度DGF（MS-DGF）的引入，利用核的不同尺度的相似性度量的能力，并避免同时找到最佳的规模。利用激光雷达数据对真实的HSI进行了沿着实验。实验结果表明，与现有的几种算法相比，该方法能够有效融合异质特征，充分利用HSI和LiDAR的互补信息，有利于城市区域的精细分类。
A novel discriminative graph-based fusion (DGF) method is proposed for urban area classification to fuse heterogeneous features from two data sources, i.e., hyperspectral image (HSI) and light detecting and ranging (LiDAR) data. The features include spectral characteristics in HSI, height in LiDAR data, and geometry in image processing technologies like morphological profiles (MPs). Our proposed DGF method couples dimension reduction and heterogeneous feature fusion. The core idea of the proposed method is to search for a projection matrix by minimizing the similarity term that preserves the local geometry of each class and maximizing the dissimilarity term that contains the relation of between-class distance. As a result, the proposed method can pull close together samples of the same class while pushing those of different classes apart in the projected space by fusing graphs constructed by different groups of heterogeneous features. The edges of the graphs are measured by kernel. Furthermore, the multiscale DGF (MS-DGF) is introduced to utilize the capability of similarity measure of different scales of kernel and avoid finding the optimal scale simultaneously. Experiments are conducted on real HSI along with LiDAR data. The corresponding results demonstrate that the proposed method can make an effective fusion of heterogeneous features to make full use of the complementary information of HSI and LiDAR, which facilitates fine classification task of urban area, compared with several state-of-the-art algorithms.