Prompting DirectSAM for Semantic Contour Extraction in Remote Sensing Images

• URL: http://arxiv.org/abs/2410.06194v1
• Date: Tue, 8 Oct 2024 16:55:42 GMT
• Title: Prompting DirectSAM for Semantic Contour Extraction in Remote Sensing Images
• Authors: Shiyu Miao, Delong Chen, Fan Liu, Chuanyi Zhang, Yanhui Gu, Shengjie Guo, Jun Zhou,
• Abstract summary: We introduce a foundation model derived from DirectSAM, termed DirectSAM-RS, which inherits the strong segmentation capability acquired from natural images. This dataset comprises over 34k image-text-contour triplets, making it at least 30 times larger than individual dataset. We evaluate the DirectSAM-RS in both zero-shot and fine-tuning setting, and demonstrate that it achieves state-of-the-art performance across several downstream benchmarks.
• Score: 11.845626002236772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Direct Segment Anything Model (DirectSAM) excels in class-agnostic contour extraction. In this paper, we explore its use by applying it to optical remote sensing imagery, where semantic contour extraction-such as identifying buildings, road networks, and coastlines-holds significant practical value. Those applications are currently handled via training specialized small models separately on small datasets in each domain. We introduce a foundation model derived from DirectSAM, termed DirectSAM-RS, which not only inherits the strong segmentation capability acquired from natural images, but also benefits from a large-scale dataset we created for remote sensing semantic contour extraction. This dataset comprises over 34k image-text-contour triplets, making it at least 30 times larger than individual dataset. DirectSAM-RS integrates a prompter module: a text encoder and cross-attention layers attached to the DirectSAM architecture, which allows flexible conditioning on target class labels or referring expressions. We evaluate the DirectSAM-RS in both zero-shot and fine-tuning setting, and demonstrate that it achieves state-of-the-art performance across several downstream benchmarks.

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