Semantic-Spatial Feature Fusion with Dynamic Graph Refinement for Remote Sensing Image Captioning

• URL: http://arxiv.org/abs/2503.23453v1
• Date: Sun, 30 Mar 2025 14:14:41 GMT
• Title: Semantic-Spatial Feature Fusion with Dynamic Graph Refinement for Remote Sensing Image Captioning
• Authors: Maofu Liu, Jiahui Liu, Xiaokang Zhang,
• Abstract summary: This paper presents a semantic-spatial feature fusion with dynamic graph refinement (SFDR) method.<n>The proposed SFDR method significantly enhances the quality of the generated descriptions.<n> Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method.
• Score: 11.015244501780078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote sensing image captioning aims to generate semantically accurate descriptions that are closely linked to the visual features of remote sensing images. Existing approaches typically emphasize fine-grained extraction of visual features and capturing global information. However, they often overlook the complementary role of textual information in enhancing visual semantics and face challenges in precisely locating objects that are most relevant to the image context. To address these challenges, this paper presents a semantic-spatial feature fusion with dynamic graph refinement (SFDR) method, which integrates the semantic-spatial feature fusion (SSFF) and dynamic graph feature refinement (DGFR) modules. The SSFF module utilizes a multi-level feature representation strategy by leveraging pre-trained CLIP features, grid features, and ROI features to integrate rich semantic and spatial information. In the DGFR module, a graph attention network captures the relationships between feature nodes, while a dynamic weighting mechanism prioritizes objects that are most relevant to the current scene and suppresses less significant ones. Therefore, the proposed SFDR method significantly enhances the quality of the generated descriptions. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method. The source code will be available at https://github.com/zxk688}{https://github.com/zxk688.

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