Balancing Colors of Nonoverlapping Mosaicking Images With Generative Adversarial Networks
Remote sensing images of different moments or sensors can be stitched together to produce a new image under uniform geographic coordinate systems, where the overlapping areas were needed for color harmony. In this letter, a reference-based mosaicking method is proposed for images either with or with...
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| Veröffentlicht in: | IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5 |
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| container_title | IEEE geoscience and remote sensing letters |
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| creator | Ma, Yaobin Wei, Jingbo Huang, Xiangtao |
| description | Remote sensing images of different moments or sensors can be stitched together to produce a new image under uniform geographic coordinate systems, where the overlapping areas were needed for color harmony. In this letter, a reference-based mosaicking method is proposed for images either with or without overlapping areas. The new method introduces a low-resolution image for spectral reference that spans all the mosaicking scope. A generative adversarial network is harnessed for color harmony, which transfers all the mosaicking images to the time of the reference image for further stitch with the graph cut and pyramid gradient methods. The proposed method is compared with three color harmony methods or tools by mosaicking the red, green, and blue bands of Landsat-8 images with MODIS as the reference. The digital evaluations demonstrate that the new method outweighs other methods regarding radiometric and spectral fidelity. |
| doi_str_mv | 10.1109/LGRS.2021.3126261 |
| format | Article |
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In this letter, a reference-based mosaicking method is proposed for images either with or without overlapping areas. The new method introduces a low-resolution image for spectral reference that spans all the mosaicking scope. A generative adversarial network is harnessed for color harmony, which transfers all the mosaicking images to the time of the reference image for further stitch with the graph cut and pyramid gradient methods. The proposed method is compared with three color harmony methods or tools by mosaicking the red, green, and blue bands of Landsat-8 images with MODIS as the reference. The digital evaluations demonstrate that the new method outweighs other methods regarding radiometric and spectral fidelity.</description><subject>Color</subject><subject>Color harmony</subject><subject>Colour</subject><subject>Coordinate systems</subject><subject>Coordinates</subject><subject>deep neural networks</subject><subject>Digital imaging</subject><subject>enblend</subject><subject>Generative adversarial networks</subject><subject>Generators</subject><subject>Geographical coordinates</subject><subject>Image color analysis</subject><subject>Image resolution</subject><subject>Kernel</subject><subject>LandSat</subject><subject>Methods</subject><subject>MODIS</subject><subject>mosaicking</subject><subject>Remote sensing</subject><subject>Remote sensors</subject><subject>Satellite imagery</subject><subject>Spatial resolution</subject><subject>spatiotemporal fusion</subject><subject>Training</subject><issn>1545-598X</issn><issn>1558-0571</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kF1LwzAUhoMoOKc_QLwpeN2ZjzZNLufQOZgT_EC9Cml6Mrt1TU26if_elg2vzgvnec-BB6FLgkeEYHkznz6_jCimZMQI5ZSTIzQgaSpinGbkuM9JGqdSfJyisxBWGNNEiGyAPm91pWtT1sto4irnQ-RstHC124GvdNP0i0cXdGnWfZxt9BJC9F62X9EUavC6LXcQjYsOD9qXuooW0P44vw7n6MTqKsDFYQ7R2_3d6-Qhnj9NZ5PxPDZUsjY2IJMcSIJxnhlZWGMosWCsKbggBceGW8NS4FkqQEogwuY55BlmwLQGU7Ahut7fbbz73kJo1cptfd29VJ0GiSmjAncU2VPGuxA8WNX4cqP9ryJY9QZVb1D1BtXBYNe52ndKAPjnJcc8w5L9AUeVby4</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Ma, Yaobin</creator><creator>Wei, Jingbo</creator><creator>Huang, Xiangtao</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Color Color harmony Colour Coordinate systems Coordinates deep neural networks Digital imaging enblend Generative adversarial networks Generators Geographical coordinates Image color analysis Image resolution Kernel LandSat Methods MODIS mosaicking Remote sensing Remote sensors Satellite imagery Spatial resolution spatiotemporal fusion Training |
| title | Balancing Colors of Nonoverlapping Mosaicking Images With Generative Adversarial Networks |
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