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arXiv · 2607.06552

MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation

Jiaju Han·Ma Yaqi·Yahui Chai·Xuemeng Sun·Xin Li·Qike Zhang·Yingying Zhao·Xiang Chen·Luwei Yang·Chengyin Hu·Jiahuan Long
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Abstract

Infrared remote-sensing imagery captures intensity structure, object-background contrast, and illumination-invariant cues often invisible in RGB imagery. Yet, most remote-sensing vision-language resources and models focus on visible-band semantics, leaving infrared vision-language understanding underexplored. We introduce MonoIR-RS, a large-scale infrared remote-sensing vision-language dataset and benchmark that couples IR-aware data construction with CLIP-style contrastive adaptation and VLM instruction tuning. Built from the same source pool and split as FusionRS, MonoIR-RS retains the infrared image as the model-facing modality, yielding 600,000 synthesized infrared images and 59,032 retained IR-aware caption records. The model experiments use this retained language-supervision subset, whose captions rewrite supervision around grayscale structure and infrared-style contrast instead of RGB appearance. We show that the synthesized infrared imagery is markedly closer to real thermal imagery than a grayscale conversion on the AVIID benchmark. We fine-tune five CLIP backbones and six VLM backbones, and calibrate them against zero-shot behavior: IR-aware adaptation lifts CLIP mean recall by up to 12.8 points and drives VLM captioning IR-cue coverage to 100% while reducing residual RGB-color leakage to near zero. By isolating the infrared modality from RGB-IR dual-modal learning, MonoIR-RS offers a controlled, reproducible testbed for aligning infrared remote-sensing evidence with language.

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Jiaju Han, Ma Yaqi, Yahui Chai, Xuemeng Sun, Xin Li, Qike Zhang, Yingying Zhao, Xiang Chen, Luwei Yang, Chengyin Hu, Jiahuan Long. 2026-07-07. MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation. https://arxiv.org/abs/2607.06552

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