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 this https URL

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

RS-Mamba for Large Remote Sensing Image Dense Prediction

Object tracking is a hot topic in computer vision. Thanks to the booming of
the very high resolution (VHR) remote sensing techniques, it is now possible to
track targets of interests in satellite videos. However, since the targets in
the satellite videos are usually too small compared with the entire image, and
too similar with the background, most state-of-the-art algorithms failed to
track the target in satellite videos with a satisfactory accuracy. Due to the
fact that optical flow shows the great potential to detect even the slight
movement of the targets, we proposed a multi-frame optical flow tracker (MOFT)
for object tracking in satellite videos. The Lucas-Kanade optical flow method
was fused with the HSV color system and integral image to track the targets in
the satellite videos, while multi-frame difference method was utilized in the
optical flow tracker for a better interpretation. The experiments with three
VHR remote sensing satellite video datasets indicate that compared with
state-of-the-art object tracking algorithms, the proposed method can track the
target more accurately.

本文提出了一种新的多帧光流追踪器（MOFT）用于卫星视频中的目标跟踪，该方法融合了 Lucas-Kanade 光流方法、HSV 颜色系统和积分图像，通过对三个 VHR 遥感卫星视频数据集的实验表明，该方法比目前先进的目标跟踪算法更准确。