UV-Mamba：一种用于高分辨率遥感影像中城中村边界识别的 DCN 增强状态空间模型
UV-Mamba: A DCN-Enhanced State Space Model for Urban Village Boundary Identification in High-Resolution Remote Sensing Images
AI总结：
本文提出 UV-Mamba，通过可变形卷积增强状态空间模型并结合编码器-解码器结构识别高分辨率遥感影像中的城中村边界，在北京、西安数据集上取得最优 IoU，且推理更快、参数更少。
摘要：
由于地理环境多样、景观复杂以及居民点高密度，利用遥感影像自动识别城中村边界仍是一项极具挑战性的任务。本文提出一种新颖且高效的神经网络模型 UV-Mamba，用于在高分辨率遥感影像中进行精确边界检测。UV-Mamba 通过引入可变形卷积，缓解了状态空间模型中随图像尺寸增大而在长序列建模时出现的记忆丢失问题。其架构采用编码器-解码器框架，编码器包含四个可变形状态空间增强块，用于高效的多层级语义提取，解码器则整合所提取的语义信息。我们在两个大型数据集上进行了实验，结果表明 UV-Mamba 取得了最先进的性能。具体而言，该模型在北京和西安数据集上分别达到 73.3% 和 78.1% 的 IoU，相比此前最佳模型分别提升 1.2% 和 3.4% 的 IoU，同时推理速度快 6 倍，参数量小 40 倍。源代码和预训练模型可在 https://github.com/Devin-Egber/UV-Mamba 获取。
英文摘要：
Due to the diverse geographical environments, intricate landscapes, and high-density settlements, the automatic identification of urban village boundaries using remote sensing images remains a highly challenging task. This paper proposes a novel and efficient neural network model called UV-Mamba for accurate boundary detection in high-resolution remote sensing images. UV-Mamba mitigates the memory loss problem in lengthy sequence modeling, which arises in state space models with increasing image size, by incorporating deformable convolutions. Its architecture utilizes an encoder-decoder framework and includes an encoder with four deformable state space augmentation blocks for efficient multi-level semantic extraction and a decoder to integrate the extracted semantic information. We conducted experiments on two large datasets showing that UV-Mamba achieves state-of-the-art performance. Specifically, our model achieves 73.3% and 78.1% IoU on the Beijing and Xi'an datasets, respectively, representing improvements of 1.2% and 3.4% IoU over the previous best model while also being 6x faster in inference speed and 40x smaller in parameter count. Source code and pre-trained models are available at https://github.com/Devin-Egber/UV-Mamba.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
