论文标题
无监督的多分支胶囊用于高光谱和激光雷达分类
Unsupervised multi-branch Capsule for Hyperspectral and LiDAR classification
论文作者
论文摘要
借助遥感数据的方便可用性，如何制作模型来解释复杂的遥感数据会引起广泛的关注。在遥感数据中，高光谱图像包含光谱信息，LIDAR包含高程信息。因此，有必要进行更多探索以更好地融合不同源数据的功能。在本文中，我们将语义理解引入了来自两个不同来源的动态融合数据，即通过不同的胶囊网络分支提取HSI和LIDAR的特征，并改善规范胶囊中的自我监督损失和随机的刚性旋转，从而使其具有高维情况。规范胶囊通过置换等值的关注计算对象的胶囊分解，并且通过随机旋转的对象的训练对来自我监视该过程。在将HSI和LIDAR的特征与语义理解融合在一起之后，实现了光谱空间 - 高度融合特征的无监督提取。通过两个现实世界的HSI和LIDAR融合的例子，实验结果表明，拟议的多支球长高维规范胶囊算法可以有效地了解HSI和LIDAR。这表明该模型可以有效提取HSI和LIDAR数据特征，而不是现有的模型，用于未经监督的多源RS数据。
With the convenient availability of remote sensing data, how to make models to interpret complex remote sensing data attracts wide attention. In remote sensing data, hyperspectral images contain spectral information and LiDAR contains elevation information. Hence, more explorations are warranted to better fuse the features of different source data. In this paper, we introduce semantic understanding to dynamically fuse data from two different sources, extract features of HSI and LiDAR through different capsule network branches and improve self-supervised loss and random rigid rotation in Canonical Capsule to a high-dimensional situation. Canonical Capsule computes the capsule decomposition of objects by permutation-equivariant attention and the process is self-supervised by training pairs of randomly rotated objects. After fusing the features of HSI and LiDAR with semantic understanding, the unsupervised extraction of spectral-spatial-elevation fusion features is achieved. With two real-world examples of HSI and LiDAR fused, the experimental results show that the proposed multi-branch high-dimensional canonical capsule algorithm can be effective for semantic understanding of HSI and LiDAR. It indicates that the model can extract HSI and LiDAR data features effectively as opposed to existing models for unsupervised extraction of multi-source RS data.
