Attribute-Based Learning for Remote Sensing Image Captioning in Unseen Scenes
Remote sensing image captioning (RSIC) aims to describe ground objects and scenes within remote sensing images in natural language form. As the complexity and diversity of scenes in remote sensing images increase, existing methods, although effective in specific tasks, are largely trained on particu...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/7/1237 |
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| Summary: | Remote sensing image captioning (RSIC) aims to describe ground objects and scenes within remote sensing images in natural language form. As the complexity and diversity of scenes in remote sensing images increase, existing methods, although effective in specific tasks, are largely trained on particular scene images and corpora. This limits their ability to generate descriptions for scenes not encountered during training. Given the finite resources for data annotation and the expanding range of application scenarios, training data typically cover only a subset of common scenes, leaving many potential scene types unrepresented. Consequently, developing models capable of effectively handling unseen scenes with limited training data is imperative. This study introduces an innovative remote sensing image captioning model based on scene attribute learning—SALCap. The proposed model defines scene attributes and employs a specifically designed global object scene attribute extractor to capture these attributes. It then uses an attribute inference module to predict scene information through scene attributes, ensuring that this part of the scene’s information is reused in sentence generation through additional attribute loss. Experiments show that the method not only improves the accuracy of the description but also significantly enhances the model’s adaptability and generalizability relative to unseen scenes. This advancement expands the practical utility of remote sensing image captioning across diverse scenarios, particularly under the constraints of limited annotations. |
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| ISSN: | 2072-4292 |