An Enhanced Classification Method Based on Adaptive Multi-Scale Fusion for Long-tailed Multispectral Point Clouds

TL;DR

This paper introduces an advanced classification approach for multispectral point clouds that employs adaptive multi-scale fusion and hybrid loss functions to improve accuracy on long-tailed outdoor datasets.

Contribution

It proposes a novel adaptive multi-scale fusion framework with a grid-balanced sampling and hybrid loss, specifically designed for long-tailed multispectral point cloud classification.

Findings

Outperforms state-of-the-art methods on three MPC datasets.

Effectively handles long-tailed class distributions.

Improves small class recognition through adaptive weighting.

Abstract

Multispectral point cloud (MPC) captures 3D spatial-spectral information from the observed scene, which can be used for scene understanding and has a wide range of applications. However, most of the existing classification methods were extensively tested on indoor datasets, and when applied to outdoor datasets they still face problems including sparse labeled targets, differences in land-covers scales, and long-tailed distributions. To address the above issues, an enhanced classification method based on adaptive multi-scale fusion for MPCs with long-tailed distributions is proposed. In the training set generation stage, a grid-balanced sampling strategy is designed to reliably generate training samples from sparse labeled datasets. In the feature learning stage, a multi-scale feature fusion module is proposed to fuse shallow features of land-covers at different scales, addressing the issue of losing fine features due to scale variations in land-covers. In the classification stage, an adaptive hybrid loss module is devised to utilize multi-classification heads with adaptive weights to balance the learning ability of different classes, improving the classification performance of small classes due to various-scales and long-tailed distributions in land-covers. Experimental results on three MPC datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods.