DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jaehyup | ko |
dc.contributor.author | Seo, Soomin | ko |
dc.contributor.author | Kim, Munchurl | ko |
dc.date.accessioned | 2022-12-03T05:01:19Z | - |
dc.date.available | 2022-12-03T05:01:19Z | - |
dc.date.created | 2022-12-03 | - |
dc.date.issued | 2021-06-23 | - |
dc.identifier.citation | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021, pp.10166 - 10174 | - |
dc.identifier.issn | 1063-6919 | - |
dc.identifier.uri | http://hdl.handle.net/10203/301525 | - |
dc.description.abstract | Pan-sharpening is a process of merging a highresolution (HR) panchromatic (PAN) image and its corresponding low-resolution (LR) multi-spectral (MS) image to create an HR-MS and pan-sharpened image. However, due to the different sensors’ locations, characteristics and acquisition time, PAN and MS image pairs often tend to have various amounts of misalignment. Conventional deeplearning-based methods that were trained with such misaligned PAN-MS image pairs suffer from diverse artifacts such as double-edge and blur artifacts in the resultant PANsharpened images. In this paper, we propose a novel framework called shift-invariant pan-sharpening with moving object alignment (SIPSA-Net) which is the first method to take into account such large misalignment of moving object regions for PAN sharpening. The SISPA-Net has a feature alignment module (FAM) that can adjust one feature to be aligned to another feature, even between the two different PAN and MS domains. For better alignment in pansharpened images, a shift-invariant spectral loss is newly designed, which ignores the inherent misalignment in the original MS input, thereby having the same effect as optimizing the spectral loss with a well-aligned MS image. Extensive experimental results show that our SIPSA-Net can generate pan-sharpened images with remarkable improvements in terms of visual quality and alignment, compared to the state-of-the-art methods. | - |
dc.language | English | - |
dc.publisher | The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) | - |
dc.title | SIPSA-Net: Shift-Invariant Pan Sharpening with Moving Object Alignment for Satellite Imagery | - |
dc.type | Conference | - |
dc.identifier.wosid | 000742075000016 | - |
dc.identifier.scopusid | 2-s2.0-85123220748 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 10166 | - |
dc.citation.endingpage | 10174 | - |
dc.citation.publicationname | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1109/CVPR46437.2021.01003 | - |
dc.contributor.localauthor | Kim, Munchurl | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.