Towards a multimodal framework for remote sensing image change retrieval and captioning

• URL: http://arxiv.org/abs/2406.13424v1
• Date: Wed, 19 Jun 2024 10:30:56 GMT
• Title: Towards a multimodal framework for remote sensing image change retrieval and captioning
• Authors: Roger Ferrod, Luigi Di Caro, Dino Ienco,
• Abstract summary: We propose a novel foundation model for bi-temporal RS image pairs, in the context of change detection analysis. By jointly training a contrastive encoder and captioning decoder, our model add text-image retrieval capabilities, in the context of bi-temporal change detection.
• Score: 3.3488510654648453
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, there has been increasing interest in multimodal applications that integrate text with other modalities, such as images, audio and video, to facilitate natural language interactions with multimodal AI systems. While applications involving standard modalities have been extensively explored, there is still a lack of investigation into specific data modalities such as remote sensing (RS) data. Despite the numerous potential applications of RS data, including environmental protection, disaster monitoring and land planning, available solutions are predominantly focused on specific tasks like classification, captioning and retrieval. These solutions often overlook the unique characteristics of RS data, such as its capability to systematically provide information on the same geographical areas over time. This ability enables continuous monitoring of changes in the underlying landscape. To address this gap, we propose a novel foundation model for bi-temporal RS image pairs, in the context of change detection analysis, leveraging Contrastive Learning and the LEVIR-CC dataset for both captioning and text-image retrieval. By jointly training a contrastive encoder and captioning decoder, our model add text-image retrieval capabilities, in the context of bi-temporal change detection, while maintaining captioning performances that are comparable to the state of the art. We release the source code and pretrained weights at: https://github.com/rogerferrod/RSICRC.

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