# Hybrid Mamba–Graph Fusion with Multi-Stage Pseudo-Label Refinement for Semi-Supervised Hyperspectral–LiDAR Classification

## TL;DR

This paper introduces a new deep learning framework that combines hyperspectral and LiDAR data using a hybrid model and pseudo-label refinement, achieving better classification accuracy with very few labeled examples.

## Contribution

The novel HMGF-Net architecture integrates Mamba state-space modules and graph fusion with a multi-stage pseudo-label refinement pipeline for semi-supervised hyperspectral–LiDAR classification.

## Key findings

- HMGF-Net achieves state-of-the-art classification accuracy on three benchmark datasets with up to 99% overall accuracy.
- The hybrid model combining Mamba and graph-based fusion improves boundary delineation and reduces noise in classification maps.
- Multi-stage pseudo-label refinement significantly enhances performance in low-label regimes with only ten labeled samples per class.

## Abstract

What are the main findings?

Novel Hybrid Architecture for HSI–LiDAR Fusion: We propose HMGF-Net, a unified multimodal network combining a spectral–spatial CNN (HSI) and a multi-scale CNN (LiDAR) with a Mamba state-space sequence module and a graph-based fusion layer to capture both local and long-range contextual features across modalities.

Multi-Stage Pseudo-Label Refinement: We develop a three-stage semi-supervised training pipeline that progressively refines pseudolabels using confidence-based filtering, spatial–spectral smoothing via a KNN graph, and graph-consistency checks, effectively denoising labels and stabilizing training with very limited ground truth.

What is the implication of the main findings?

State-of-the-Art Performance with Limited Labels:The proposed approach achieves superior classification accuracy on three benchmark hyperspectral–LiDAR datasets (Houston2013, Augsburg, Trento), outperforming eight recent state-of-the-art methods. Notably, HMGF-Net attains higher overall accuracy (up to 92–99%) and average accuracy than competitors, with pronounced gains in low-label regimes (only ten labeled
samples per class).

Synergy of Sequence Modelling and Graph Reasoning: Our results demonstrate that integrating long-range sequence modeling (Mamba) with graph-based context propagation yields smoother classification maps and better class discrimination. The hybrid approach reduces salt-and-pepper noise and improves boundary delineation in the predicted maps, highlighting its effectiveness for detailed sensor data analysis.

Semi-supervised joint classification of Hyperspectral Images (HSIs) and LiDAR-derived Digital Surface Models (DSMs) remains challenging due to scarcity of labeled pixels, strong intra-class variability, and the heterogeneous nature of spectral and elevation features. In this work, we propose a Hybrid Mamba–Graph Fusion Network (HMGF-Net) with Multi-Stage Pseudo-Label Refinement (MS-PLR) for semi-supervised hyperspectral–LiDAR classification. The framework employs a spectral–spatial HSI backbone combining 3D–2D convolutions, a compact LiDAR CNN encoder, Mamba-style state-space sequence blocks for long-range spectral and cross-modal dependency modeling, and a graph fusion module that propagates information over a heterogeneous pixel graph. Semi-supervised learning is realized via a three-stage pseudolabeling pipeline that progressively filters, smooths, and re-weights pseudolabels based on prediction confidence, spatial–spectral consistency, and graph neighborhood agreement. We validate HMGF-Net on three benchmark hyperspectral–LiDAR datasets. Compared with a set of eight state-of-the-art (SOTA) baselines, including 3D-CNNs, SSRN, HybridSN, transformer-based models such as SpectralFormer, multimodal CNN–GCN fusion networks, and recent semi-supervised methods, the proposed approach delivers consistent gains in overall accuracy, average accuracy, and Cohen’s kappa, especially in low-label regimes (10% labeled pixels). The results highlight that the synergy between sequence modeling and graph reasoning in combination with carefully designed pseudolabel refinement is essential to maximizing the benefit of abundant unlabeled samples in multimodal remote sensing scenarios.