File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Improving the spatial resolution of landsat TM/ETM+ through fusion with SPOT5 images via learning-based super-resolution

TitleImproving the spatial resolution of landsat TM/ETM+ through fusion with SPOT5 images via learning-based super-resolution
Authors
KeywordsLandsat Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+) image
Spatial resolution
Super-resolution
Swath width
Système Pour l'Observation de la Terre 5 (SPOT5) image
Issue Date2015
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2015, v. 53, n. 3, p. 1195-1204 How to Cite?
AbstractTo take advantage of the wide swath width of Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) images and the high spatial resolution of Système Pour l'Observation de la Terre 5 (SPOT5) images, we present a learning-based super-resolution method to fuse these two data types. The fused images are expected to be characterized by the swath width of TM/ETM+ images and the spatial resolution of SPOT5 images. To this end, we first model the imaging process from a SPOT image to a TM/ETM+ image at their corresponding bands, by building an image degradation model via blurring and downsampling operations. With this degradation model, we can generate a simulated Landsat image from each SPOT5 image, thereby avoiding the requirement for geometric coregistration for the two input images. Then, band by band, image fusion can be implemented in two stages: 1) learning a dictionary pair representing the high- and low-resolution details from the given SPOT5 and the simulated TM/ETM+ images; 2) super-resolving the input Landsat images based on the dictionary pair and a sparse coding algorithm. It is noteworthy that the proposed method can also deal with the conventional spatial and spectral fusion of TM/ETM+ and SPOT5 images by using the learned dictionary pairs. To examine the performance of the proposed method of fusing the swath width of TM/ETM+ and the spatial resolution of SPOT5, we illustrate the fusion results on the actual TM images and compare with several classic pansharpening methods by assuming that the corresponding SPOT5 panchromatic image exists. Furthermore, we implement the classification experiments on both actual images and fusion results to demonstrate the benefits of the proposed method for further classification applications.
Persistent Identifierhttp://hdl.handle.net/10722/329334
ISSN
2023 Impact Factor: 7.5
2023 SCImago Journal Rankings: 2.403
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSong, Huihui-
dc.contributor.authorHuang, Bo-
dc.contributor.authorLiu, Qingshan-
dc.contributor.authorZhang, Kaihua-
dc.date.accessioned2023-08-09T03:32:03Z-
dc.date.available2023-08-09T03:32:03Z-
dc.date.issued2015-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2015, v. 53, n. 3, p. 1195-1204-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/329334-
dc.description.abstractTo take advantage of the wide swath width of Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) images and the high spatial resolution of Système Pour l'Observation de la Terre 5 (SPOT5) images, we present a learning-based super-resolution method to fuse these two data types. The fused images are expected to be characterized by the swath width of TM/ETM+ images and the spatial resolution of SPOT5 images. To this end, we first model the imaging process from a SPOT image to a TM/ETM+ image at their corresponding bands, by building an image degradation model via blurring and downsampling operations. With this degradation model, we can generate a simulated Landsat image from each SPOT5 image, thereby avoiding the requirement for geometric coregistration for the two input images. Then, band by band, image fusion can be implemented in two stages: 1) learning a dictionary pair representing the high- and low-resolution details from the given SPOT5 and the simulated TM/ETM+ images; 2) super-resolving the input Landsat images based on the dictionary pair and a sparse coding algorithm. It is noteworthy that the proposed method can also deal with the conventional spatial and spectral fusion of TM/ETM+ and SPOT5 images by using the learned dictionary pairs. To examine the performance of the proposed method of fusing the swath width of TM/ETM+ and the spatial resolution of SPOT5, we illustrate the fusion results on the actual TM images and compare with several classic pansharpening methods by assuming that the corresponding SPOT5 panchromatic image exists. Furthermore, we implement the classification experiments on both actual images and fusion results to demonstrate the benefits of the proposed method for further classification applications.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.subjectLandsat Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+) image-
dc.subjectSpatial resolution-
dc.subjectSuper-resolution-
dc.subjectSwath width-
dc.subjectSystème Pour l'Observation de la Terre 5 (SPOT5) image-
dc.titleImproving the spatial resolution of landsat TM/ETM+ through fusion with SPOT5 images via learning-based super-resolution-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TGRS.2014.2335818-
dc.identifier.scopuseid_2-s2.0-84907457526-
dc.identifier.volume53-
dc.identifier.issue3-
dc.identifier.spage1195-
dc.identifier.epage1204-
dc.identifier.isiWOS:000343900600006-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats