GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.
Semantic-segmentation-of-remote-sensing-image 基于深度学习关于遥感影像的语义分割 首先看一下数据集,包含原始影像与标签,实际的分辨率很大,这个只是缩略图。 影像数据是Landsat8卫星的,用五四三波段进行合成,并利用GS方法进行全色第八波段的融合。(Envi软件处理) 标签是通过矢量图层以ArcGIS软件来处理生成的。 此代码...
Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation 参考遥感图像分割的旋转多尺度交互网络 参考遥感图像分割 (RRSIS)是一个新的挑战,它结合了计算机视觉和自然语言处理,通过文本查询描述了航空图像中的特定区域。传统的参考图像分割(RIS)方法受到了航空图像中复杂的空间尺度和方向的...
Jin, M., Wang, P., Li, Y.: Hya-gan: remote sensing image cloud removal based on hybrid attention generation adversarial network. Int. J. Remote Sens. 45(6), 1755–1773 (2024) Article Google Scholar Pan, H.: Cloud removal for remote sensing imagery via spatial attention generative adv...
REMOTE sensingAMBIGUITYWith the development of deep learning, Remote Sensing Image (RSI) semantic segmentation has produced significant advances. However, due to the sparse distribution of the objects and the high similarity between classes, the task of semantic segmentation in RSI is still extremely ...
开创性工作:Y. Sun, S. Feng, X. Li, Y. Ye, J. Kang and X. Huang, "Visual grounding in remote sensing images",Proc. 30th ACM Int. Conf. Multimedia, pp. 404-412, 2022. contribution: 提出遥感数据视觉定位 (RSVG) 任务 构建了新的 RSVG 大型基准数据集 ...
Stitched remote sensing images often exhibit irregular boundaries, which can be frustrating for general users and detrimental to downstream tasks such as object detection and segmentation. However, this issue has received insufficient attention and remains unexplored within the remote sensing domain. In th...
The source code is available at https://github.com/Youzhihui/RFANet. Introduction Change detection, as one of the favored tasks in remote sensing image processing, focuses on identifying disparities between bi-temporal or multi-temporal images of the same geographical region, which are taken at ...
GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.
Curriculum-style Local-to-global Adaptation for Cross-domain Remote Sensing Image Segmentation - BOBrown/CCDA_LGFA