基于视觉Mamba与多尺度多频率特征融合的遥感图像分割
Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion
AI总结：
针对遥感图像分割中精度与计算复杂度难以平衡的问题，提出结合视觉Mamba与卷积的混合网络CVMH-UNet，通过交叉扫描捕获全局信息、多频多尺度融合提升细节，在低复杂度下实现更优分割性能。
摘要：
随着遥感成像技术的不断发展演进，处理高分辨率、多样化的卫星影像以提升分割精度和解译效率，已成为遥感领域的关键研究方向。尽管基于CNN和Transformer的分割算法在性能上取得了显著进展，但在分割精度和计算复杂度之间取得平衡仍存在挑战，限制了其在实际任务中的广泛应用。针对这一问题，本文引入状态空间模型（SSM），提出了一种基于视觉Mamba的新型混合语义分割网络CVMH-UNet。该方法设计了交叉扫描视觉状态空间块（CVSSBlock），通过交叉二维扫描（CS2D）从多个方向充分捕获全局信息，同时通过引入卷积神经网络分支克服Vision Mamba（VMamba）在局部信息获取上的局限，实现全局与局部特征的综合分析。此外，针对直接跳跃连接判别能力有限、难以实现细节融合的问题，设计了多频率多尺度特征融合块（MFMSBlock）。该模块通过二维离散余弦变换（2D DCT）引入多频率信息以提升信息利用率，并通过逐点卷积分支提供额外尺度的局部细节信息，最终沿通道维度聚合多尺度信息，实现精细化特征融合。在知名遥感影像数据集上开展的实验结果表明，所提出的CVMH-UNet在保持低计算复杂度的同时实现了优异的分割性能，性能超越当前主流的前沿分割算法。
英文摘要：
As remote sensing imaging technology continues to advance and evolve, processing high-resolution and diversified satellite imagery to improve segmentation accuracy and enhance interpretation efficiency emerg as a pivotal area of investigation within the realm of remote sensing. Although segmentation algorithms based on CNNs and Transformers achieve significant progress in performance, balancing segmentation accuracy and computational complexity remains challenging, limiting their wide application in practical tasks. To address this, this paper introduces state space model (SSM) and proposes a novel hybrid semantic segmentation network based on vision Mamba (CVMH-UNet). This method designs a cross-scanning visual state space block (CVSSBlock) that uses cross 2D scanning (CS2D) to fully capture global information from multiple directions, while by incorporating convolutional neural network branches to overcome the constraints of Vision Mamba (VMamba) in acquiring local information, this approach facilitates a comprehensive analysis of both global and local features. Furthermore, to address the issue of limited discriminative power and the difficulty in achieving detailed fusion with direct skip connections, a multi-frequency multi-scale feature fusion block (MFMSBlock) is designed. This module introduces multi-frequency information through 2D discrete cosine transform (2D DCT) to enhance information utilization and provides additional scale local detail information through point-wise convolution branches. Finally, it aggregates multi-scale information along the channel dimension, achieving refined feature fusion. Findings from experiments conducted on renowned datasets of remote sensing imagery demonstrate that proposed CVMH-UNet achieves superior segmentation performance while maintaining low computational complexity, outperforming surpassing current leading-edge segmentation algorithms.
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。
