Abstract
The advancement of deep learning has driven notable progress in remote sensing semantic segmentation. Attention mechanisms, while enabling global modeling and utilizing contextual information, face challenges of high computational costs and require window-based operations that weaken capturing long-range dependencies, hindering their effectiveness for remote sensing image processing. In this letter, we propose AMMUNet, a UNet-based framework that employs multi-scale attention map merging, comprising two key innovations: the granular multi-head self-attention (GMSA) module and the attention map merging mechanism (AMMM). GMSA efficiently acquires global information while substantially mitigating computational costs in contrast to global multi-head self-attention mechanism. This is accomplished through the strategic utilization of dimension correspondence to align granularity and the reduction of relative position bias parameters, thereby optimizing computational efficiency. The proposed AMMM effectively combines multi-scale attention maps into a unified representation using a fixed mask template, enabling the modeling of global attention mechanism. Experimental evaluations highlight the superior performance of our approach, achieving remarkable mean intersection over union (mIoU) scores of 75.48\% on the challenging Vaihingen dataset and an exceptional 77.90\% on the Potsdam dataset, demonstrating the superiority of our method in precise remote sensing semantic segmentation. Codes are available at this https URL.
Abstract (translated)
深度学习的进步在远程 sensing语义分割方面取得了显著的进展。尽管引入了注意机制，实现了全局建模并利用上下文信息，但高计算成本的挑战和基于窗口的操作削弱了远程感应在图像处理中的有效性。在本文中，我们提出了AMMUNet，一种基于UNet的框架，采用多尺度注意力图合并，包括两个关键创新：粒度多头自注意力（GMSA）模块和注意力图合并机制（AMMM）。GMSA有效地获取全球信息，而与全局多头自注意力机制相比，大大降低了计算成本。这是通过将维度对应性用于对齐粒度并减少相对位置偏差参数来实现的，从而优化了计算效率。与AMMM结合的AMMUNet能够将多尺度注意力图合并为统一的表示，实现对全局注意机制的建模。实验评估显示，我们的方法在具有挑战性的Vaihingen数据集和Potsdam数据集上取得了卓越的性能，平均交集 over 联合（mIoU）得分分别为75.48%和77.90%，证明了我们在远程感应在语义分割方面的优越性。代码可在此处访问：https://www.acm.org/dl/2022.pdf
URL
https://arxiv.org/abs/2404.13408
