The spatial resolution of remote sensing images is becoming increasingly higher, posing challenges in handling large very-high-resolution (VHR) remote sensing images for dense prediction tasks. Models based on convolutional neural networks are limited in their ability to model global features of remote sensing images due to local convolution operations. Transformer based models, despite their global modeling capabilities, face computational challenges with large VHR images due to their quadratic complexity. The common practice of cropping large images into smaller patches leads to a significant loss of contextual information. To address these issues, we propose the Remote Sensing Mamba (RSM) for dense prediction tasks in VHR remote sensing. RSM is designed to model global features of remote sensing images with linear complexity, enabling it to process large VHR images effectively. It employs an omnidirectional selective scan module to globally model the images in multiple directions, capturing large spatial features from various directions. Experiments on semantic segmentation and change detection tasks across various objects demonstrate the effectiveness of RSM. With simple model architecture and training approach, RSM achieves state-of-the-art performance on the dense prediction tasks of VHR remote sensing. The code for this work will be available at https://github.com/walking-shadow/Official_Remote_Sensing_Mamba.

远程感知图像中的密集预测任务存在挑战，本研究提出了一种基于远程感知Mamba（RSM）的模型，利用线性复杂度对全局特征进行建模，以有效处理大尺寸的高空间分辨率远程感知图像，并通过全局建模和多方向扫描提取大尺度空间特征等，实验结果表明该方法在VHR远程感知的密集预测任务上取得了最先进的性能。

大型遥感影像密集预测的RS-Mamba