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

• URL: http://arxiv.org/abs/2606.09123v1
• Date: Mon, 08 Jun 2026 07:18:38 GMT
• Title: An Enhanced Geometric-Spectral Feature Learning Framework for Airborne Multispectral Point Cloud Classification
• Abstract summary: We build two MPC datasets and propose an enhanced geometric-spectral feature learning framework based on attentions for airborne MPC classification.<n>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.<n> Experimental results on two airborne MPC datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods.
• Score: 51.004475033276215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Quantum Enchanced Multi-Scale CNN with Bi-directional Mamba for Crop Field Analysis [0.06372261626436675]
  Hyperspectral image (HSI) crop analysis is essential for precision agriculture.<n>HSI classification remains challenging due to high spectral dimensionality, spatial complexity, class imbalance, and limited labeled samples.<n>This paper proposes a BiSpectral Mamba-based framework that combines multi-scale convolutional feature extraction, spectral attention, bidirectional state-space modeling, and quantum-inspired learning.
  arXiv  Detail & Related papers  (2026-06-15T19:11:09Z)
• Multiview Manifold Evidential Fusion for PolSAR Image Classification [51.41332458376411]
  We propose a new framework to integrate PolSAR manifold learning and evidence fusion into a unified architecture.<n>Experiments on three real-world PolSAR datasets demonstrate that the proposed method consistently outperforms existing approaches in accuracy, robustness, and interpretability.
  arXiv  Detail & Related papers  (2025-10-13T09:05:51Z)
• Multispectral State-Space Feature Fusion: Bridging Shared and Cross-Parametric Interactions for Object Detection [48.04749955821739]
  A novel Multispectral State-Space Feature Fusion framework, dubbed MS2Fusion, is proposed.<n>MS2Fusion achieves efficient and effective fusion through a dual-path parametric interaction mechanism.<n>In our experiments on mainstream benchmarks, our MS2Fusion significantly outperforms other state-of-the-art multispectral object detection methods.
  arXiv  Detail & Related papers  (2025-07-19T14:38:03Z)
• Pillar-Voxel Fusion Network for 3D Object Detection in Airborne Hyperspectral Point Clouds [35.24778377226701]
  We propose PiV-A HPC, a 3D object detection network for airborne HPCs.<n>We first develop a pillar-voxel dual-branch encoder, where the former captures spectral and vertical structural features from HPCs to overcome spectral distortion.<n>A multi-level feature fusion mechanism is devised to enhance information interaction between the two branches.
  arXiv  Detail & Related papers  (2025-04-13T10:13:48Z)
• PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detection [59.355022416218624]
  integration of point and voxel representations is becoming more common in LiDAR-based 3D object detection. We propose a novel two-stage 3D object detector, called Point-Voxel Attention Fusion Network (PVAFN) PVAFN uses a multi-pooling strategy to integrate both multi-scale and region-specific information effectively.
  arXiv  Detail & Related papers  (2024-08-26T19:43:01Z)
• Hierarchical Attention and Parallel Filter Fusion Network for Multi-Source Data Classification [33.26466989592473]
  We propose a hierarchical attention and parallel filter fusion network for multi-source data classification. Our proposed method achieves 91.44% and 80.51% of overall accuracy (OA) on the respective datasets.
  arXiv  Detail & Related papers  (2024-08-22T23:14:22Z)
• Hyperspectral Image Reconstruction via Combinatorial Embedding of Cross-Channel Spatio-Spectral Clues [6.580484964018551]
  Existing learning-based hyperspectral reconstruction methods show limitations in fully exploiting the information among the hyperspectral bands. We propose to investigate the inter-dependencies in their respective hyperspectral space. These embedded features can be fully exploited by querying the inter-channel correlations.
  arXiv  Detail & Related papers  (2023-12-18T11:37:19Z)
• Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation [55.9217962930169]
  We present S2ADet, an object detector that harnesses the rich spectral and spatial complementary information inherent in hyperspectral images. S2ADet surpasses existing state-of-the-art methods, achieving robust and reliable results.
  arXiv  Detail & Related papers  (2023-06-14T09:01:50Z)
• Centralized Feature Pyramid for Object Detection [53.501796194901964]
  Visual feature pyramid has shown its superiority in both effectiveness and efficiency in a wide range of applications. In this paper, we propose a OLO Feature Pyramid for object detection, which is based on a globally explicit centralized feature regulation.
  arXiv  Detail & Related papers  (2022-10-05T08:32:54Z)
• Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra [68.8204255655161]
  We focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We uncover interesting structures in the principal component basis, namely, well-defined branches corresponding to different chemical regimes.
  arXiv  Detail & Related papers  (2022-01-07T22:26:33Z)
• Spatial-Spectral Manifold Embedding of Hyperspectral Data [43.479889860715275]
  We propose a novel hyperspectral embedding approach by simultaneously considering spatial and spectral information. spatial-spectral manifold embedding (SSME) models the spatial and spectral information jointly in a patch-based fashion. SSME not only learns the spectral embedding by using the adjacency matrix obtained by similarity measurement between spectral signatures, but also models the spatial neighbours of a target pixel in hyperspectral scene.
  arXiv  Detail & Related papers  (2020-07-17T05:40:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.