Enhancing Perception of Key Changes in Remote Sensing Image Change Captioning

• URL: http://arxiv.org/abs/2409.12612v1
• Date: Thu, 19 Sep 2024 09:33:33 GMT
• Title: Enhancing Perception of Key Changes in Remote Sensing Image Change Captioning
• Authors: Cong Yang, Zuchao Li, Hongzan Jiao, Zhi Gao, Lefei Zhang,
• Abstract summary: We propose a novel framework for remote sensing image change captioning, guided by Key Change Features and Instruction-tuned (KCFI) KCFI includes a ViTs encoder for extracting bi-temporal remote sensing image features, a key feature perceiver for identifying critical change areas, and a pixel-level change detection decoder. To validate the effectiveness of our approach, we compare it against several state-of-the-art change captioning methods on the LEVIR-CC dataset.
• Score: 49.24306593078429
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, while significant progress has been made in remote sensing image change captioning, existing methods fail to filter out areas unrelated to actual changes, making models susceptible to irrelevant features. In this article, we propose a novel multimodal framework for remote sensing image change captioning, guided by Key Change Features and Instruction-tuned (KCFI). This framework aims to fully leverage the intrinsic knowledge of large language models through visual instructions and enhance the effectiveness and accuracy of change features using pixel-level change detection tasks. Specifically, KCFI includes a ViTs encoder for extracting bi-temporal remote sensing image features, a key feature perceiver for identifying critical change areas, a pixel-level change detection decoder to constrain key change features, and an instruction-tuned decoder based on a large language model. Moreover, to ensure that change description and change detection tasks are jointly optimized, we employ a dynamic weight-averaging strategy to balance the losses between the two tasks. We also explore various feature combinations for visual fine-tuning instructions and demonstrate that using only key change features to guide the large language model is the optimal choice. To validate the effectiveness of our approach, we compare it against several state-of-the-art change captioning methods on the LEVIR-CC dataset, achieving the best performance. Our code will be available at https://github.com/yangcong356/KCFI.git.

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