MANet: Fine-Tuning Segment Anything Model for Multimodal Remote Sensing Semantic Segmentation 格式:PDF 页数:12 上传日期:2024-11-13 08:19:08 浏览次数:5 下载积分:199 加入阅读清单 100% 还剩11 页未读,是否继续阅读? 此文档由 leo_wyomin.. 分享于 2024-11-13...
Semantic segmentation is an essential technique in remote sensing. Until recently, most related research has focused primarily on advancing semantic segmentation models based on monomodal imagery, and less attention has been given to models that utilize multimodal remote sensing data. Moreover, most curr...
IMAGE segmentationAutomatic matching of multimodal remote sensing images remains a vital yet challenging task, particularly for remote sensing and computer vision applications. Most traditional methods mainly focus on key point detection and description of the original image, thus ignoring the deep semantic...
A multimodal (i.e., Sentinel-2, Sentinel-1, and SRTM) remote sensing dataset in Hunan, China. - LauraChow/HunanMultimodalDataset
Weickert J (2001) Efficient image segmentation using partial differential equations and morphology. Pattern Recognit 34(9):1813–1824 Article Google Scholar Beis JS, Lowe DG (1997) Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In: Proceedings of IEEE computer...
The various milestones in the evolution of deep learning significantly promote the advancement of semantic segmentation research. Moreover, the availability of multiple sensing modalities has encouraged the development of multimodal fusion, such as 3D LiDARs, RGB-D cameras, thermal cameras, etc. These ...
While F1-score and IoU are one of the most commonly used metrics for remote sensing semantic segmentation tasks, we also considered MAE and MAPE metrics. They provide us better understanding and interpretable values for burned area. The absolute error of the predicted burned area is increased ...
Recent methods, such as RustQNet, which analyzes multimodal remote sensing images for the quantitative inversion of the wheat stripe rust disease index [7], and HighDAN, which utilizes the multimodal remote sensing dataset (C2Seg) to enhance model generalization and segmentation performance in cross...
Progressive fusion learning: A multimodal joint segmentation framework for building extraction from optical and SAR images Automatic and high-precision extraction of buildings from remote sensing images has a wide range of application and importance. Optical and synthetic apert... X Li,G Zhang,H Cui...
Federated Modality-Specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation Q Dai, D Wei, H Liu, J Sun, L Wang, Y Zheng AAAI, 2024 PUB 跨模态检索 Cross-modal Retrieval FedCMR: Federated cross-modal retrieval L Zong, Q Xie, J Zhou, P Wu, X Zhang, B Xu SIGIR...