# An Enhanced Geometric-Spectral Feature Learning Framework for Airborne Multispectral Point Cloud Classification

arXiv: https://arxiv.org/abs/2606.09123 · HTML: https://mlsanity.com/paper/2606.09123/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 52 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Builds on (most cited first, 22 of 22)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [PointConv: Deep Convolutional Networks on 3D Point Clouds](https://mlsanity.com/paper/1811.07246/) (2018)
- [Point Transformer V3: Simpler, Faster, Stronger](https://mlsanity.com/paper/2312.10035/) (2023)
- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019)
- [PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies](https://mlsanity.com/paper/2206.04670/) (2022)
- [Point Transformer V2: Grouped Vector Attention and Partition-based Pooling](https://mlsanity.com/paper/2210.05666/) (2022)
- [A high-resolution canopy height model of the Earth](https://mlsanity.com/paper/2204.08322/) (2022)
- [Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion](https://mlsanity.com/paper/2103.07074/) (2021)
- [An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing](https://mlsanity.com/paper/1810.12000/) (2018)
- [Point Cloud Mamba: Point Cloud Learning via State Space Model](https://mlsanity.com/paper/2403.00762/) (2024)
- [LitePT: Lighter Yet Stronger Point Transformer](https://mlsanity.com/paper/2512.13689/) (2025)
- [Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification](https://mlsanity.com/paper/2207.10278/) (2022)
- [GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modeling](https://mlsanity.com/paper/2207.06766/) (2022)
- [Spherical Frustum Sparse Convolution Network for LiDAR Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2311.17491/) (2023)
- [Point Tree Transformer for Point Cloud Registration](https://mlsanity.com/paper/2406.17530/) (2024)
- [Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scene](https://mlsanity.com/paper/2301.12972/) (2023)
- [Multilateral Cascading Network for Semantic Segmentation of Large-Scale Outdoor Point Clouds](https://mlsanity.com/paper/2409.13983/) (2024)
- [An Enhanced Classification Method Based on Adaptive Multi-Scale Fusion for Long-tailed Multispectral Point Clouds](https://mlsanity.com/paper/2412.11407/) (2024)
- [LPRnet: A self-supervised registration network for LiDAR and photogrammetric point clouds](https://mlsanity.com/paper/2501.05669/) (2025)
- [Pillar-Voxel Fusion Network for 3D Object Detection in Airborne Hyperspectral Point Clouds](https://mlsanity.com/paper/2504.09506/) (2025)

## Abstract (arXiv)

Multispectral point cloud (MPC) is composed of 3D spatial-spectral information, which holds tremendous potential for accurate land-cover classification. However, the representation power of classification models is limited by inherent high-dimensional and heterogeneous spatial-spectral information, unbalanced sample distribution, and inter-class spectral similarity of airborne MPCs. We build two MPC datasets and propose an enhanced geometric-spectral feature learning framework based on attentions for airborne MPC classification. A key component in our model is a two-stream feature fusion method with attention mechanisms, which enhances the representation capability of spatial-spectral features from high-dimensional heterogeneous MPCs. The first stream aims to extract position-encoded global spectral features with fusion self-attention, and the second stream comprises a multikernel point convolution and feature aggregation attention to extract spectral-guided geometric features. We then develop a residual attention fusion block to integrate the most informative geometric-spectral features from the two parallel streams. Another important contribution of this work is a joint loss function to improve the learning ability on unbalanced and interclass similar samples. Experimental results on two airborne MPC datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods. Furthermore, the codes and datasets used in this paper will be made available freely at https://github.com/HITlixian/TGRS_GSFF.