Popeye: A Unified Visual-Language Model for Multi-Source Ship Detection from Remote Sensing Imagery

Ship detection needs to identify ship locations from remote sensing (RS) scenes. However, due to different imaging payloads, various appearances of ships, and complicated background interference from the bird's eye view, it is difficult to set up a unified paradigm for achieving multi-source ship detection. Therefore, in this article, considering that the large language models (LLMs) emerge the powerful generalization ability, a novel unified visual-language model called Popeye is proposed for multi-source ship detection from RS imagery. First, to bridge the interpretation gap between multi-source images for ship detection, a novel image-instruction-answer way is designed to integrate the various ship detection ways (e.g., horizontal bounding box (HBB), oriented bounding box (OBB)) into a unified labeling paradigm. Then, in view of this, a cross-modal image interpretation method is developed for the proposed Popeye to enhance interactive comprehension ability between visual and language content, which can be easily migrated into any multi-source ship detection task. Subsequently, owing to objective domain differences, a knowledge adaption mechanism is designed to adapt the pre-trained visual-language knowledge from the nature scene into the RS domain for multi-source ship detection. In addition, the segment anything model (SAM) is also seamlessly integrated into the proposed Popeye to achieve pixel-level ship segmentation without additional training costs. Finally, extensive experiments are conducted on the newly constructed instruction dataset named MMShip, and the results indicate that the proposed Popeye outperforms current specialist, open-vocabulary, and other visual-language models for zero-shot multi-source ship detection.

EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain

Multi-modal large language models (MLLMs) have demonstrated remarkable success in vision and visual-language tasks within the natural image domain. Owing to the significant diversities between the natural and remote sensing (RS) images, the development of MLLMs in the RS domain is still in the infant stage. To fill the gap, a pioneer MLLM named EarthGPT integrating various multi-sensor RS interpretation tasks uniformly is proposed in this paper for universal RS image comprehension. In EarthGPT, three key techniques are developed including a visual-enhanced perception mechanism, a cross-modal mutual comprehension approach, and a unified instruction tuning method for multi-sensor multi-task in the RS domain. More importantly, a dataset named MMRS-1M featuring large-scale multi-sensor multi-modal RS instruction-following is constructed, comprising over 1M image-text pairs based on 34 existing diverse RS datasets and including multi-sensor images such as optical, synthetic aperture radar (SAR), and infrared. The MMRS-1M dataset addresses the drawback of MLLMs on RS expert knowledge and stimulates the development of MLLMs in the RS domain. Extensive experiments are conducted, demonstrating the EarthGPT's superior performance in various RS visual interpretation tasks compared with the other specialist models and MLLMs, proving the effectiveness of the proposed EarthGPT and offering a versatile paradigm for open-set reasoning tasks.