Referring Remote Sensing Image Segmentation via Bidirectional Alignment Guided Joint Prediction

• URL: http://arxiv.org/abs/2502.08486v1
• Date: Wed, 12 Feb 2025 15:21:18 GMT
• Title: Referring Remote Sensing Image Segmentation via Bidirectional Alignment Guided Joint Prediction
• Authors: Tianxiang Zhang, Zhaokun Wen, Bo Kong, Kecheng Liu, Yisi Zhang, Peixian Zhuang, Jiangyun Li,
• Abstract summary: ours is a novel framework designed to bridge the vision-language gap, enhance multi-scale feature interaction, and improve fine-grained object differentiation. Experiments on the benchmark datasets RefSegRS and RRSIS-D demonstrate that ours achieves state-of-the-art performance.
• Score: 7.8862197224709805
• License:
• Abstract: Referring Remote Sensing Image Segmentation (RRSIS) is critical for ecological monitoring, urban planning, and disaster management, requiring precise segmentation of objects in remote sensing imagery guided by textual descriptions. This task is uniquely challenging due to the considerable vision-language gap, the high spatial resolution and broad coverage of remote sensing imagery with diverse categories and small targets, and the presence of clustered, unclear targets with blurred edges. To tackle these issues, we propose \ours, a novel framework designed to bridge the vision-language gap, enhance multi-scale feature interaction, and improve fine-grained object differentiation. Specifically, \ours introduces: (1) the Bidirectional Spatial Correlation (BSC) for improved vision-language feature alignment, (2) the Target-Background TwinStream Decoder (T-BTD) for precise distinction between targets and non-targets, and (3) the Dual-Modal Object Learning Strategy (D-MOLS) for robust multimodal feature reconstruction. Extensive experiments on the benchmark datasets RefSegRS and RRSIS-D demonstrate that \ours achieves state-of-the-art performance. Specifically, \ours improves the overall IoU (oIoU) by 3.76 percentage points (80.57) and 1.44 percentage points (79.23) on the two datasets, respectively. Additionally, it outperforms previous methods in the mean IoU (mIoU) by 5.37 percentage points (67.95) and 1.84 percentage points (66.04), effectively addressing the core challenges of RRSIS with enhanced precision and robustness.

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