Brewing Stronger Features: Dual-Teacher Distillation for Multispectral Earth Observation

2026-02-23

AI Summary

This work addresses the challenge of effectively modeling cross-modal semantic consistency and capturing unified global representations in Earth observation pretraining. To this end, we propose a dual-teacher contrastive distillation framework that, for the first time, introduces contrastive self-distillation into multispectral remote sensing. Our approach leverages both a multispectral-specific model and a general-purpose vision foundation model as teachers to guide the student in learning consistent semantic representations across optical and multispectral data. This is further enhanced by an improved masked image modeling strategy and a cross-modal alignment mechanism. Extensive experiments demonstrate consistent improvements of 3.64%, 1.20%, and 1.31% on average over state-of-the-art methods in semantic segmentation, change detection, and classification tasks, respectively, establishing new performance benchmarks in multimodal remote sensing scenarios.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Foundation models are transforming Earth Observation (EO), yet the diversity of EO sensors and modalities makes a single universal model unrealistic. Multiple specialized EO foundation models (EOFMs) will likely coexist, making efficient knowledge transfer across modalities essential. Most existing EO pretraining relies on masked image modeling, which emphasizes local reconstruction but provides limited control over global semantic structure. To address this, we propose a dual-teacher contrastive distillation framework for multispectral imagery that aligns the student's pretraining objective with the contrastive self-distillation paradigm of modern optical vision foundation models (VFMs). Our approach combines a multispectral teacher with an optical VFM teacher, enabling coherent cross-modal representation learning. Experiments across diverse optical and multispectral benchmarks show that our model adapts to multispectral data without compromising performance on optical-only inputs, achieving state-of-the-art results in both settings, with an average improvement of 3.64 percentage points in semantic segmentation, 1.2 in change detection, and 1.31 in classification tasks. This demonstrates that contrastive distillation provides a principled and efficient approach to scalable representation learning across heterogeneous EO data sources. Code: Coming soon.

Problem

• Earth Observation
• multispectral imagery
• foundation models
• cross-modal representation
• knowledge distillation

Innovation

• dual-teacher distillation
• contrastive distillation
• multispectral Earth Observation
• foundation models
• cross-modal representation learning