MKANet: Multiscale Kernel Aggregation Network for Multispectral LiDAR 3-D Point Cloud Scene Recognition

Multispectral light detection and ranging (LiDAR) is an active sensor that can simultaneously obtain spatial geometric data and multiwavelength information, offering multiattribute features for geoinformation products such as point cloud scene recognition. Despite these advantages, semantic segmentation of fine-grained point cloud scenes in multispectral LiDAR data remains a challenging task, mainly due to the disordered arrangement of point cloud and the large amount of data involved. In particular, it is crucial to extract high-quality features from unstructured point cloud for semantic segmentation. To address these challenges, this article proposes a multiscale kernel aggregation network (MKANet) for point cloud semantic segmentation and evaluates its performance in real scenes. Specifically, MKANet is similar to convolutional kernel operation, which extracts point cloud features by establishing correlation coefficients between point cloud and kernel point, and then aggregates point cloud features into kernels according to coefficient weights. The designed MKANet is a polyhedral structure composed of multiple kernel points, and its interior is multilayer distributed kernel points. Moreover, the kernel points aggregate the features by the distance weight from the surrounding point cloud. Therefore, multiscale kernel points provide a more diverse distribution and also provide the feasibility of aggregating features more widely. In addition, multiscale kernel aggregation can provide more kernel points with different distributions to reduce the confusion caused by the weight contribution of similar points. To further objectively evaluate the effectiveness and performance of MKANet, we conduct a series of comparative experiments using real-world multispectral LiDAR point cloud data. The results of these experiments highlight the critical and influential role of MKANet in scene recognition of multispectral LiDAR point cloud and have reached numerical values of performance.