This article presents a deep residual network-based fusion framework for hyperspectral and LiDAR data. In this framework, three new fusion methods are proposed, which are the residual network-based deep feature fusion (RNDFF), the residual network-based probability reconstruction fusion (RNPRF) and the residual network-based probability multiplication fusion (RNPMF). The three methods use extinction profile (EP), local binary pattern (LBP), and deep residual network. Specifically, EP and LBP features are extracted from two sources and stacked as spatial features. For RNDFF, the deep features of each source are extracted by a deep residual network, and then the deep features are stacked to create the fusion features which are classified by softmax classifier. For RNPRF, the deep features of each source are input to the softmax classifier to obtain the probability matrices, and then the probability matrices are fused by weighted addition to producing the final label assignment. For RNPMF, the probability matrices are fused by array multiplication. Experimental results demonstrate that the classification performance of the proposed methods significantly outperform existing methods in hyperspectral and LiDAR data fusion.

中文翻译：

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基于深度残差网络的高光谱和LiDAR数据融合框架

本文介绍了针对高光谱和LiDAR数据的基于深度残差网络的融合框架。在此框架下，提出了三种新的融合方法，分别是基于残差网络的深度特征融合（RNDFF），基于残差网络的概率重构融合（RNPRF）和基于残差网络的概率乘法融合（RNPMF）。这三种方法使用消光轮廓（EP），局部二进制模式（LBP）和深残留网络。具体而言，从两个来源提取EP和LBP特征并将其堆叠为空间特征。对于RNDFF，通过深度残差网络提取每个源的深度特征，然后将这些深度特征堆叠以创建融合特征，并通过softmax分类器对其进行分类。对于RNPRF，将每个源的深层特征输入到softmax分类器以获得概率矩阵，然后通过加权加法将概率矩阵融合以生成最终标签分配。对于RNPMF，概率矩阵通过数组乘法进行融合。实验结果表明，在高光谱和LiDAR数据融合中，所提出方法的分类性能明显优于现有方法。