# Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data Classification

arXiv: https://arxiv.org/abs/2301.03335 · HTML: https://mlsanity.com/paper/2301.03335/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 9 Jan 2023.

## Builds on (most cited first, 9 of 9)

- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Deep Modular Co-Attention Networks for Visual Question Answering](https://mlsanity.com/paper/1906.10770/) (2019)
- [TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech](https://mlsanity.com/paper/2007.06028/) (2020)
- [More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification](https://mlsanity.com/paper/2008.05457/) (2020)
- [HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification](https://mlsanity.com/paper/1902.06701/) (2019)
- [Classification of Hyperspectral and LiDAR Data Using Coupled CNNs](https://mlsanity.com/paper/2002.01144/) (2020)
- [A3CLNN: Spatial, Spectral and Multiscale Attention ConvLSTM Neural Network for Multisource Remote Sensing Data Classification](https://mlsanity.com/paper/2204.04462/) (2022)
- [Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification](https://mlsanity.com/paper/1905.04554/) (2019)

## Built on by (most cited first, 12 of 12)

- [SS-MAE: Spatial-Spectral Masked Auto-Encoder for Multi-Source Remote Sensing Image Classification](https://mlsanity.com/paper/2311.04442/) (2023)
- [Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks](https://mlsanity.com/paper/2312.06068/) (2023)
- [Multimodal Informative ViT: Information Aggregation and Distribution for Hyperspectral and LiDAR Classification](https://mlsanity.com/paper/2401.03179/) (2024)
- [Dynamic Frequency Feature Fusion Network for Multi-Source Remote Sensing Data Classification](https://mlsanity.com/paper/2507.04510/) (2025)
- [Contrastive Learning for Regression on Hyperspectral Data](https://mlsanity.com/paper/2403.17014/) (2024)
- [A Contrastive Learning Foundation Model Based on Perfectly Aligned Sample Pairs for Remote Sensing Images](https://mlsanity.com/paper/2505.19447/) (2025)
- [Boosting Multimodal Remote Sensing Image Classification with Transformer-based Heterogeneously Salient Graph Representation](https://mlsanity.com/paper/2311.10320/) (2023)
- [FusDreamer: Label-efficient Remote Sensing World Model for Multimodal Data Classification](https://mlsanity.com/paper/2503.13814/) (2025)
- [A Diff-Attention Aware State Space Fusion Model for Remote Sensing Classification](https://mlsanity.com/paper/2504.16665/) (2025)
- [Prototype-Based Information Compensation Network for Multi-Source Remote Sensing Data Classification](https://mlsanity.com/paper/2505.04003/) (2025)
- [Spectral-Spatial Contrastive Learning Framework for Regression on Hyperspectral Data](https://mlsanity.com/paper/2602.10745/) (2026)
- [From Articles to Canopies: Knowledge-Driven Pseudo-Labelling for Tree Species Classification using LLM Experts](https://mlsanity.com/paper/2604.16115/) (2026)

## Abstract (arXiv)

The joint hyperspectral image (HSI) and LiDAR data classification aims to interpret ground objects at more detailed and precise level. Although deep learning methods have shown remarkable success in the multisource data classification task, self-supervised learning has rarely been explored. It is commonly nontrivial to build a robust self-supervised learning model for multisource data classification, due to the fact that the semantic similarities of neighborhood regions are not exploited in existing contrastive learning framework. Furthermore, the heterogeneous gap induced by the inconsistent distribution of multisource data impedes the classification performance. To overcome these disadvantages, we propose a Nearest Neighbor-based Contrastive Learning Network (NNCNet), which takes full advantage of large amounts of unlabeled data to learn discriminative feature representations. Specifically, we propose a nearest neighbor-based data augmentation scheme to use enhanced semantic relationships among nearby regions. The intermodal semantic alignments can be captured more accurately. In addition, we design a bilinear attention module to exploit the second-order and even high-order feature interactions between the HSI and LiDAR data. Extensive experiments on four public datasets demonstrate the superiority of our NNCNet over state-of-the-art methods. The source codes are available at urlhttps://github.com/summitgao/NNCNet.