arXiv:1707.09959
Correction of "Cloud Removal By Fusing Multi-Source and Multi-Temporal Images"
원문 초록
Remote sensing images often suffer from cloud cover. Cloud removal is required in many applications of remote sensing images. Multitemporal-based methods are popular and effective to cope with thick clouds. This paper contributes to a summarization and experimental comparation of the existing multitemporal-based methods. Furthermore, we propose a spatiotemporal-fusion with poisson-adjustment method to fuse multi-sensor and multi-temporal images for cloud removal. The experimental results show that the proposed method has potential to address the problem of accuracy reduction of cloud removal in multi-temporal images with significant changes.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Image Restoration via Multi-domain Learning
Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design Space
Attentive Contextual Attention for Cloud Removal
AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite Imagery
Multi-scale Restoration of Missing Data in Optical Time-series Images with Masked Spatial-Temporal Attention Network
Bridging Remote Sensors with Multisensor Geospatial Foundation Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.