提出SGTN网络，提升遥感图像实例分割的边界精度与全局关联能力。
A Novel Shape Guided Transformer Network for Instance Segmentation in Remote Sensing Images
- 设计LSwin编码器，融合垂直水平全局自注意力，增强遥感图像全局感知。
- 引入形状引导模块，强化目标边界与形状信息，提升实例分割精度。
- 在多个遥感数据集上表现最优，适合高精度遥感目标分割任务。
遥感图像（RSIs）中的实例分割性能受两大因素影响：如何从受动态大气干扰的遥感影像中提取准确的对象边界，以及如何整合分散在广阔空间区域内的相关对象实例之间的互信息。本文提出一种新型形状引导变压器网络（SGTN），以实现更精确的实例级对象提取。受自注意力机制全局上下文建模能力的启发，提出一种名为LSwin的有效变压器编码器，其结合了垂直与水平1D全局自注意力机制，在全局感知能力上优于主流的基于局部移位窗口的Swin Transformer。为实现精确的实例掩码分割，引入形状引导模块（SGM），突出对象边界和形状信息。SGM强调局部细节信息，而LSwin关注全局上下文关系，两者结合取得优异的遥感图像实例分割效果。通过全面的消融实验验证了其有效性。尤其，LSwin在同等效率下优于流行的ResNet和Swin Transformer编码器。相比其他实例分割方法，本模型在两个单类公共数据集（WHU数据集、BITCC数据集）和一个多类公共数据集（NWPU VHR-10数据集）上均取得最高平均精度（AP）得分。代码将发布于 http://gpcv.whu.edu.cn/data/。
原文摘要 · Abstract (English)
Instance segmentation performance in remote sensing images (RSIs) is significantly affected by two issues: how to extract accurate boundaries of objects from remote imaging through the dynamic atmosphere, and how to integrate the mutual information of related object instances scattered over a vast spatial region. In this study, we propose a novel Shape Guided Transformer Network (SGTN) to accurately extract objects at the instance level. Inspired by the global contextual modeling capacity of the self-attention mechanism, we propose an effective transformer encoder termed LSwin, which incorporates vertical and horizontal 1D global self-attention mechanisms to obtain better global-perception capacity for RSIs than the popular local-shifted-window based Swin Transformer. To achieve accurate instance mask segmentation, we introduce a shape guidance module (SGM) to emphasize the object boundary and shape information. The combination of SGM, which emphasizes the local detail information, and LSwin, which focuses on the global context relationships, achieve excellent RSI instance segmentation. Their effectiveness was validated through comprehensive ablation experiments. Especially, LSwin is proved better than the popular ResNet and Swin transformer encoder at the same level of efficiency. Compared to other instance segmentation methods, our SGTN achieves the highest average precision (AP) scores on two single-class public datasets (WHU dataset and BITCC dataset) and a multi-class public dataset (NWPU VHR-10 dataset). Code will be available at http://gpcv.whu.edu.cn/data/.
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