Optimized Spatial Gradient Transfer for Hyperspectral-LiDAR Data Classification

Remote Sensing

Bing Tu¹  Siyuan Chen¹  Chengle Zhou¹  Yu Zhu¹  Antonio Plaza² 

[1] College of Information and Communication Engineering, Hunan Institute of Science and Technology, Yueyang 414000, China;Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, Escuela Politecnica, University of Extremadura, E-10003 Caceres, Spain;

关键词: data fusion;    gradient transfer;    superpixel;    hyperspectral image;    LiDAR data;   

DOI  :  10.3390/rs14081814

来源: DOAJ

【 摘 要 】

The classification accuracy of ground objects is improved due to the combined use of the same scene data collected by different sensors. We propose to fuse the spatial planar distribution and spectral information of the hyperspectral images (HSIs) with the spatial 3D information of the objects captured by light detection and ranging (LiDAR). In this paper, we use the optimized spatial gradient transfer method for data fusion, which can effectively solve the strong heterogeneity of heterogeneous data fusion. The entropy rate superpixel segmentation algorithm over-segments HSI and LiDAR to extract local spatial and elevation information, and a Gaussian density-based regularization strategy normalizes the local spatial and elevation information. Then, the spatial gradient transfer model and $l^1$-total variation minimization are introduced to realize the fusion of local multi-attribute features of different sources, and fully exploit the complementary information of different features for the description of ground objects. Finally, the fused local spatial features are reconstructed into a guided image, and the guided filtering acts on each dimension of the original HSI, so that the output maintains the complete spectral information and detailed changes of the spatial fusion features. It is worth mentioning that we have carried out two versions of expansion on the basis of the proposed method to improve the joint utilization of multi-source data. Experimental results on two real datasets indicated that the fused features of the proposed method have a better effect on ground object classification than the mainstream stacking or cascade fusion methods.

【 授权许可】

Unknown