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MMLGNet: Cross-Modal Alignment of Remote Sensing Data using CLIP

Aditya Chaudhary, Sneha Barman, Mainak Singha, Ankit Jha, Girish Mishra, Biplab Banerjee

TL;DR

This work addresses the challenge of semantically interpreting heterogeneous remote sensing data by aligning HSI and LiDAR with natural language through a CLIP-inspired framework. MMLGNet uses modality-specific CNN encoders, a fusion module, and a frozen CLIP text encoder to learn a shared latent space via symmetric contrastive learning, enabling language-guided semantic interpretation of multisensor RS data. The authors demonstrate consistent gains over visual-only baselines on MUUFL Gulfport and Trento, with comprehensive ablations validating the benefits of language supervision, multimodal fusion, and prompt-level text encoding. The results suggest that language-informed multimodal representations can enhance semantic understanding and generalization in geospatial analysis, with future work exploring prompt learning to further boost performance.

Abstract

In this paper, we propose a novel multimodal framework, Multimodal Language-Guided Network (MMLGNet), to align heterogeneous remote sensing modalities like Hyperspectral Imaging (HSI) and LiDAR with natural language semantics using vision-language models such as CLIP. With the increasing availability of multimodal Earth observation data, there is a growing need for methods that effectively fuse spectral, spatial, and geometric information while enabling semantic-level understanding. MMLGNet employs modality-specific encoders and aligns visual features with handcrafted textual embeddings in a shared latent space via bi-directional contrastive learning. Inspired by CLIP's training paradigm, our approach bridges the gap between high-dimensional remote sensing data and language-guided interpretation. Notably, MMLGNet achieves strong performance with simple CNN-based encoders, outperforming several established multimodal visual-only methods on two benchmark datasets, demonstrating the significant benefit of language supervision. Codes are available at https://github.com/AdityaChaudhary2913/CLIP_HSI.

MMLGNet: Cross-Modal Alignment of Remote Sensing Data using CLIP

TL;DR

This work addresses the challenge of semantically interpreting heterogeneous remote sensing data by aligning HSI and LiDAR with natural language through a CLIP-inspired framework. MMLGNet uses modality-specific CNN encoders, a fusion module, and a frozen CLIP text encoder to learn a shared latent space via symmetric contrastive learning, enabling language-guided semantic interpretation of multisensor RS data. The authors demonstrate consistent gains over visual-only baselines on MUUFL Gulfport and Trento, with comprehensive ablations validating the benefits of language supervision, multimodal fusion, and prompt-level text encoding. The results suggest that language-informed multimodal representations can enhance semantic understanding and generalization in geospatial analysis, with future work exploring prompt learning to further boost performance.

Abstract

In this paper, we propose a novel multimodal framework, Multimodal Language-Guided Network (MMLGNet), to align heterogeneous remote sensing modalities like Hyperspectral Imaging (HSI) and LiDAR with natural language semantics using vision-language models such as CLIP. With the increasing availability of multimodal Earth observation data, there is a growing need for methods that effectively fuse spectral, spatial, and geometric information while enabling semantic-level understanding. MMLGNet employs modality-specific encoders and aligns visual features with handcrafted textual embeddings in a shared latent space via bi-directional contrastive learning. Inspired by CLIP's training paradigm, our approach bridges the gap between high-dimensional remote sensing data and language-guided interpretation. Notably, MMLGNet achieves strong performance with simple CNN-based encoders, outperforming several established multimodal visual-only methods on two benchmark datasets, demonstrating the significant benefit of language supervision. Codes are available at https://github.com/AdityaChaudhary2913/CLIP_HSI.
Paper Structure (12 sections, 6 equations, 3 figures, 5 tables)

This paper contains 12 sections, 6 equations, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Preliminaries
  4. Our proposed MMLGNet
  5. Modality-Specific Visual Feature Extraction
  6. Multimodal Visual Feature Fusion
  7. Language-Vision Alignment
  8. Network Optimization
  9. Experimental Evaluations
  10. Comparison to Literature
  11. Ablation Studies
  12. Conclusion and Future Work

Figures (3)

  • Figure 1: Architecture overivew of our multimodal CLIP-based MMLGNet.
  • Figure 2: Ablation study on the LiDAR input channels.
  • Figure 3: Classification maps for HSI, LiDAR, ground truth, and MMLGNet predictions on (a) MUUFL and (b) Trento datasets.