Graph Attention Feature Fusion Network for ALS Point Cloud Classification

Abstract

Classification is a fundamental task for airborne laser scanning (ALS) point cloud processing and applications. This task is challenging due to outdoor scenes with high complexity and point clouds with irregular distribution. Many existing methods based on deep learning techniques have drawbacks, such as complex pre/post-processing steps, an expensive sampling cost, and a limited receptive field size. In this paper, we propose a graph attention feature fusion network (GAFFNet) that can achieve a satisfactory classification performance by capturing wider contextual information of the ALS point cloud. Based on the graph attention mechanism, we first design a neighborhood feature fusion unit and an extended neighborhood feature fusion block, which effectively increases the receptive field for each point. On this basis, we further design a neural network based on encoder–decoder architecture to obtain the semantic features of point clouds at different levels, allowing us to achieve a more accurate classification. We evaluate the performance of our method on a publicly available ALS point cloud dataset provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). The experimental results show that our method can effectively distinguish nine types of ground objects. We achieve more satisfactory results on different evaluation metrics when compared with the results obtained via other approaches.

Deep learning based computer vision under the prism of 3D point clouds: a systematic review

“…Continuous methods define a convolutional kernel in a continuous space, where the neighboring points' weights are determined based on their spatial distribution regarding the center point. These methods can be translated as a weighted sum over a given subset [180,181]. 3D discrete methods describe convolutional kernels on conventional grids, and the neighboring points' weights are determined based on the offsets from the center point [182].…”

A Multiscale Multi-Feature Deep Learning Model for Airborne Point-Cloud Semantic Segmentation

“…Li et al [21] designed a refined feature extractor using a self-attention mechanism to improve the accuracy of point-cloud classification. Yang et al [22] proposed a graph attention feature fusion network (GAFFNet) that could achieve a satisfactory classification performance by capturing a wider range of contextual information of the ALS point cloud. Luo et al [23] confirmed the potential of using multispectral LiDAR in the classification of complex urban land cover through three comparison methods.…”