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Large vision language model: enhanced-RSCLIP with exemplar-image prompting for uncommon object detection in satellite imagery

Research Output: Contribution to journal Article Peer-review

Open access

Abstract

Large Vision Language Models (LVLMs) have shown promise in remote sensing applications, yet struggle with “uncommon” objects that lack sufficient public labeled data. This paper presents Enhanced-RSCLIP, a novel dual-prompt architecture that combines text prompting with exemplar-image processing for cattle herd detection in satellite imagery. Our approach introduces a key innovation where an exemplar-image preprocessing module using crop-based or attention-based algorithms extracts focused object features which are fed as a dual stream to a contrastive learning framework that fuses textual descriptions with visual exemplar embeddings. We evaluated our method on a custom dataset of 260 satellite images across UK and Nigerian regions. Enhanced-RSCLIP with crop-based exemplar processing achieved 72% accuracy in cattle detection and 56.2% overall accuracy on cross-domain transfer tasks, significantly outperforming text-only CLIP (31% overall accuracy). The dual-prompt architecture enables effective few-shot learning and cross-regional transfer from data-rich (UK) to data-sparse (Nigeria) environments, demonstrating a 41% improvement over baseline approaches for uncommon object detection in satellite imagery.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Article number

3071

Journal (Volume, Issue Number)

Electronics (Switzerland) (Volume 14, Issue 15)

Publication milestones

  • Accepted/In press - 14/07/2025
  • Published - 31/07/2025

Publication status

Published - 31/07/2025

ISSN

2079-9292

Publication IDs

  • handle.net: 10547/626762
  • Scopus: 105013095061

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