Name
Title | ||
|---|---|---|
Regularized Multiresolution Spatial Unmixing for ENVISAT/MERIS and Landsat/TM Image Fusion |
Abstract | ||
|---|---|---|
Earth observation satellites currently provide a large volume of images at different scales. Most of these satellites provide global coverage with a revisit time that usually depends on the instrument characteristics and performance. Typically, medium-spatial-resolution instruments provide better spectral and temporal resolutions than mapping-oriented high-spatial-resolution multispectral sensors. However, in order to monitor a given area of interest, users demand images with the best resolution available, which cannot be reached using a single sensor. In this context, image fusion may be effective to merge information from different data sources. In this letter, an image fusion approach based on multiresolution and multisource spatial unmixing is used to obtain a composite image with the spectral and temporal characteristics of medium-spatial-resolution instrument along with the spatial resolution of high-spatial-resolution image. A time series of Landsat/TM and ENVISAT/MERIS Full Resolution images acquired in the 2004 European Space Agency (ESA) Spectra Barrax Campaign illustrates the method's capabilities. The qualitative and quantitative assessments of the product images are given. The proposed methodology is general enough to be applied to similar sensors, such as the multispectral instruments which will fly on board the ESA GMES Sentinel-2 and Sentinel-3 upcoming satellite series. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/LGRS.2011.2120591 | Geoscience and Remote Sensing Letters, IEEE |
Keywords | Field | DocType |
geophysical image processing,image fusion,remote sensing,ENVISAT-MERIS image fusion,ESA GMES Sentinel-2,ESA GMES Sentinel-3,Earth observation satellites,European Space Agency,Landsat-TM image fusion,Spectra Barrax Campaign,composite image,data sources,medium-spatial-resolution instruments,regularized multiresolution spatial unmixing,remote sensing data,spectral resolution,temporal resolution,Data fusion,Landsat Thematic Mapper (TM),MEdium Resolution Imaging Spectrometer (MERIS),downscaling,multiresolution,spatial unmixing,subpixel | Computer vision,Satellite,Image fusion,Remote sensing,Multispectral image,Composite image filter,Sensor fusion,Pixel,Artificial intelligence,Subpixel rendering,Image resolution,Mathematics | Journal |
Volume | Issue | ISSN |
8 | 5 | 1545-598X |
Citations | PageRank | References |
13 | 0.86 | 10 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julia Amorós-López | 1 | 84 | 11.55 |
| Luis Gómez-Chova | 2 | 257 | 24.92 |
| Luis Alonso-Chorda | 3 | 71 | 2.75 |
| Luis Guanter | 4 | 373 | 42.94 |
| José F. Moreno | 5 | 356 | 53.81 |
| Gustavo Camps-Valls | 6 | 2011 | 114.02 |
| Amoros-Lopez, J. | 7 | 13 | 0.86 |
| Gomez-Chova, L. | 8 | 13 | 0.86 |
| Alonso, L. | 9 | 52 | 3.06 |