Classification of LiDAR Data Combined Octave Convolution With Capsule Network

Light Detection and Ranging (LiDAR) data are widely used for high-resolution land cover mapping, which can provide very valuable information about the height of the surveyed area for the discrim- ination of classes. In order to utilize the advantages of deep models for the classification of LiDAR-derived features, a new classification algorithm combined Octave Convolution (OctConv) with Capsule Network (CapsNet), is proposed here to hierarchically extract robust and discriminant features of the input data, called as OctConv-CapsNet. In the proposed approach, CapsNet captures the spatial information of the data, and OctConv processes separately for high- and low-frequency feature. OctConv is embedded in the primary capsule layer of CapsNet so that the proposed approach can make the most of both the spatial information and the high- and low-frequency information simultaneously. The proposed framework performs experiments on two LiDAR-DSM datasets (i.e. Bayview Park and Recology datasets). The results show that, compared with the traditional deep convolution model, OctConv-CapsNet can improve the classification accuracy of LiDAR-DSM data, and when the number of training samples of the experiment is 800, the classification accuracies reached 96.12% and 96.79% on Bayview Park and Recology datasets, respectively.