# Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

arXiv: https://arxiv.org/abs/2002.01144 · HTML: https://mlsanity.com/paper/2002.01144/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2020.

## Built on by (most cited first, 25 of 25)

- [Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review](https://mlsanity.com/paper/2205.01380/) (2022)
- [Multimodal Fusion Transformer for Remote Sensing Image Classification](https://mlsanity.com/paper/2203.16952/) (2022)
- [Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model](https://mlsanity.com/paper/2105.10196/) (2021)
- [Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data Classification](https://mlsanity.com/paper/2301.03335/) (2023)
- [LiDAR-Guided Cross-Attention Fusion for Hyperspectral Band Selection and Image Classification](https://mlsanity.com/paper/2404.03883/) (2024)
- [ASANet: Asymmetric Semantic Aligning Network for RGB and SAR image land cover classification](https://mlsanity.com/paper/2412.02044/) (2024)
- [Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation](https://mlsanity.com/paper/2306.08370/) (2023)
- [AllSpark: A Multimodal Spatio-Temporal General Intelligence Model with Ten Modalities via Language as a Reference Framework](https://mlsanity.com/paper/2401.00546/) (2023)
- [Single-stream CNN with Learnable Architecture for Multi-source Remote Sensing Data](https://mlsanity.com/paper/2109.06094/) (2021)
- [Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion with Missing Data](https://mlsanity.com/paper/2210.17311/) (2022)
- [Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications](https://mlsanity.com/paper/2301.01200/) (2022)
- [Multimodal Informative ViT: Information Aggregation and Distribution for Hyperspectral and LiDAR Classification](https://mlsanity.com/paper/2401.03179/) (2024)
- [Hyperspectral and LiDAR data classification based on linear self-attention](https://mlsanity.com/paper/2104.02301/) (2021)
- [Unsupervised multi-branch Capsule for Hyperspectral and LiDAR classification](https://mlsanity.com/paper/2206.07348/) (2022)
- [Multimodal Hyperspectral Image Classification via Interconnected Fusion](https://mlsanity.com/paper/2304.00495/) (2023)
- [Unsupervised Band Selection Using Fused HSI and LiDAR Attention Integrating With Autoencoder](https://mlsanity.com/paper/2404.05258/) (2024)
- [HyperPointFormer: Multimodal Fusion in 3D Space with Dual-Branch Cross-Attention Transformers](https://mlsanity.com/paper/2505.23206/) (2025)
- [Deep Learning and Earth Observation to Support the Sustainable Development Goals](https://mlsanity.com/paper/2112.11367/) (2021)
- [Image Transformation for IoT Time-Series Data: A Review](https://mlsanity.com/paper/2311.12742/) (2023)
- [Multimodal classification of forest biodiversity potential from 2D orthophotos and 3D airborne laser scanning point clouds](https://mlsanity.com/paper/2501.01728/) (2025)
- [FDCT: Frequency-Aware Decomposition and Cross-Modal Token-Alignment for Multi-Sensor Target Classification](https://mlsanity.com/paper/2503.09873/) (2025)
- [Prototype-Based Information Compensation Network for Multi-Source Remote Sensing Data Classification](https://mlsanity.com/paper/2505.04003/) (2025)
- [From Pixels to Images: A Structural Survey of Deep Learning Paradigms in Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2505.15147/) (2025)
- [CSFMamba: Cross State Fusion Mamba Operator for Multimodal Remote Sensing Image Classification](https://mlsanity.com/paper/2509.00677/) (2025)
- [HyDeMiC: A Deep Learning-based Mineral Classifier using Hyperspectral Data](https://mlsanity.com/paper/2601.17352/) (2026)

## Abstract (arXiv)

In this paper, we propose an efficient and effective framework to fuse hyperspectral and Light Detection And Ranging (LiDAR) data using two coupled convolutional neural networks (CNNs). One CNN is designed to learn spectral-spatial features from hyperspectral data, and the other one is used to capture the elevation information from LiDAR data. Both of them consist of three convolutional layers, and the last two convolutional layers are coupled together via a parameter sharing strategy. In the fusion phase, feature-level and decision-level fusion methods are simultaneously used to integrate these heterogeneous features sufficiently. For the feature-level fusion, three different fusion strategies are evaluated, including the concatenation strategy, the maximization strategy, and the summation strategy. For the decision-level fusion, a weighted summation strategy is adopted, where the weights are determined by the classification accuracy of each output. The proposed model is evaluated on an urban data set acquired over Houston, USA, and a rural one captured over Trento, Italy. On the Houston data, our model can achieve a new record overall accuracy of 96.03%. On the Trento data, it achieves an overall accuracy of 99.12%. These results sufficiently certify the effectiveness of our proposed model.