Unlocking Multi-Spectral Data for Multi-Modal Models with Guided Inputs and Chain-of-Thought Reasoning

2026-04-24 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Environmental Science & Earth Systems, Data Science & Analytics · Depth: Advanced, extended

Summary

A novel training-free approach enables generalist Large Multi-modal Models (LMMs), typically trained on RGB images, to process multi-spectral Remote Sensing (RS) data. This method adapts non-RGB inputs into pseudo-images and injects domain-specific information and Chain-of-Thought (CoT) reasoning as instructions during the inference pipeline. Demonstrated with the Gemini 2.5 model, the approach achieves significant Zero-Shot performance gains on popular RS benchmarks like BigEarthNet and EuroSat, outperforming existing state-of-the-art methods. The technique involves generating false-color and pseudo-color images from multi-spectral bands (e.g., Sentinel-2 L2A 12-band data), including visualizations of indices like NDWI and NDMI, and providing detailed interpretative prompts. This allows LMMs to leverage their visual understanding for specialized sensor inputs without costly retraining.

Key takeaway

For Computer Vision Engineers working with Remote Sensing data, this training-free method offers a powerful way to extend generalist LMMs like Gemini 2.5 to multi-spectral inputs. You should explore converting your multi-spectral bands into pseudo-images and integrating detailed, Chain-of-Thought prompts to achieve high Zero-Shot performance, avoiding the expense and fragility of specialized model retraining.

Key insights

Generalist LMMs can interpret multi-spectral data zero-shot by converting it to pseudo-images and using detailed, CoT-enhanced prompts.

Principles

• Adaptation via pseudo-images and textual context is effective.
• Chain-of-Thought reasoning significantly boosts LMM performance.
• Training-free methods offer resilience to sensor evolution.

Method

Transform multi-spectral data into pseudo-images (e.g., false color, NDVI, NDWI) and provide LMMs with detailed instructional prompts, including spectral band definitions, physical meanings, and a 'Propose-and-Verify' Chain-of-Thought reasoning structure.

In practice

• Generate false-color composites from NIR, SWIR bands.
• Calculate and visualize indices like NDVI, NDWI, NDMI.
• Use 'Propose-and-Verify' CoT for complex classifications.

Topics

• Multi-spectral Data
• Multi-modal Models
• Remote Sensing
• Zero-Shot Learning
• Chain-of-Thought Reasoning

Code references

• google-gemini/cookbook

Best for: Computer Vision Engineer, AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.