# L-MCAT: Unpaired Multimodal Transformer with Contrastive Attention for Label-Efficient Satellite Image Classification

arXiv: https://arxiv.org/abs/2507.20259 · HTML: https://mlsanity.com/paper/2507.20259/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 27 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2207.08051/) (2022)
- [Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency](https://mlsanity.com/paper/2206.08496/) (2022)
- [SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers](https://mlsanity.com/paper/2107.02988/) (2021)
- [Lightweight Adapter Learning for More Generalized Remote Sensing Change Detection](https://mlsanity.com/paper/2504.19598/) (2025)

## Abstract (arXiv)

We propose the Lightweight Multimodal Contrastive Attention Transformer (L-MCAT), a novel transformer-based framework for label-efficient remote sensing image classification using unpaired multimodal satellite data. L-MCAT introduces two core innovations: (1) Modality-Spectral Adapters (MSA) that compress high-dimensional sensor inputs into a unified embedding space, and (2) Unpaired Multimodal Attention Alignment (U-MAA), a contrastive self-supervised mechanism integrated into the attention layers to align heterogeneous modalities without pixel-level correspondence or labels. L-MCAT achieves 95.4% overall accuracy on the SEN12MS dataset using only 20 labels per class, outperforming state-of-the-art baselines while using 47x fewer parameters and 23x fewer FLOPs than MCTrans. It maintains over 92% accuracy even under 50% spatial misalignment, demonstrating robustness for real-world deployment. The model trains end-to-end in under 5 hours on a single consumer GPU.