{ "article_id": 651, "public_id": "ca5801a6-5c96-4df0-8015-db125f33da13", "internal_identifier": null, "source_identifier": "39:16", "status": "published", "type": "research_article", "title": "Modified Synthetic Variable Ratio Pansharpening Method", "title_secondary": null, "abstract": "Pansharpening, which is transferring the spatial content of a high-resolution panchromatic (PAN) band into a lowerresolution multispectral (MS) image to produce a spatially enhanced MS image, has always been one of the hottest topics of\r\nimage processing. Numerous studies have focused on developing approaches to inject the spatial details with minimum spectral\r\ndistortion. This study utilized the Genetic Algorithms (GA) to improve the performance of the Synthetic Variable Ratio (SVR),\r\nwhich is one of the most conventional pansharpening methods. This method was modified such that the weight of each MS band\r\nwas estimated by means of a GA to achieve the optimum result. The spectral quality of the image produced by the proposed\r\napproach was compared against those of the images obtained from widely-used pansharpening algorithms Principal Component\r\nAnalysis (PCA), Modified IHS (MIHS), Gram-Schmidt (GS), Nearest Neighbor Diffuse (NND), High-Pass Filtering (HPF) and\r\nconventional SVR. The quantitative evaluation of the pansharpening results revealed that the proposed approach resulted in\r\nsuperior spectral quality, compared to the other methods.  ", "language": "en", "english_title": null, "english_abstract": null, "doi": null, "doi_url": null, "resource_url": "https://www.set-science.com/a/ca5801a6-5c96-4df0-8015-db125f33da13", "pdf_url": "https://www.set-science.com/a/ca5801a6-5c96-4df0-8015-db125f33da13/pdf", "og_image": "https://www.set-science.com/assets/img/og-card-v2.png", "authors": [ { "public_id": "8d38fb06-cb34-4438-aff2-8f92508de486", "given": "Volkan", "family": "Yılmaz", "full": "Volkan Yılmaz", "literal": null, "orcid": null, "orcid_url": null, "email": "volkanyilmaz.jdz@gmail.com", "corresponding": true, "sequence": "first", "affiliations": [ { "name": "Karadeniz Teknik University", "department": null, "country": null, "country_code": null, "ror": null, "ror_url": null } ] }, { "public_id": "53e141f8-6837-44a4-a877-7f1150b9a933", "given": "Çiğdem", "family": "Şerifoğlu Yılmaz", "full": "Çiğdem Şerifoğlu Yılmaz", "literal": null, "orcid": null, "orcid_url": null, "email": "volkanyilmaz.jdz@gmail.com", "corresponding": false, "sequence": "additional", "affiliations": [ { "name": "Karadeniz Teknik University", "department": null, "country": null, "country_code": null, "ror": null, "ror_url": null } ] }, { "public_id": "e6df0bd6-bba1-4740-a59d-8a57717232e3", "given": "Oğuz", "family": "Güngör", "full": "Oğuz Güngör", "literal": null, "orcid": null, "orcid_url": null, "email": "volkanyilmaz.jdz@gmail.com", "corresponding": false, "sequence": "additional", "affiliations": [ { "name": "Karadeniz Teknik University", "department": null, "country": null, "country_code": null, "ror": null, "ror_url": null } ] } ], "keywords": [ "pansharpening, genetic algorithm, synthetic variable ratio, image enhancement, image fusion" ], "translations": [ { "language": "en", "title": "Modified Synthetic Variable Ratio Pansharpening Method", "abstract": "Pansharpening, which is transferring the spatial content of a high-resolution panchromatic (PAN) band into a lowerresolution multispectral (MS) image to produce a spatially enhanced MS image, has always been one of the hottest topics of\r\nimage processing. Numerous studies have focused on developing approaches to inject the spatial details with minimum spectral\r\ndistortion. This study utilized the Genetic Algorithms (GA) to improve the performance of the Synthetic Variable Ratio (SVR),\r\nwhich is one of the most conventional pansharpening methods. This method was modified such that the weight of each MS band\r\nwas estimated by means of a GA to achieve the optimum result. The spectral quality of the image produced by the proposed\r\napproach was compared against those of the images obtained from widely-used pansharpening algorithms Principal Component\r\nAnalysis (PCA), Modified IHS (MIHS), Gram-Schmidt (GS), Nearest Neighbor Diffuse (NND), High-Pass Filtering (HPF) and\r\nconventional SVR. The quantitative evaluation of the pansharpening results revealed that the proposed approach resulted in\r\nsuperior spectral quality, compared to the other methods.  \r\n", "keywords": [ "pansharpening, genetic algorithm, synthetic variable ratio, image enhancement, image fusion" ] } ], "subjects": [], "subject_names": [], "published_date": "2018-12-31", "published_year": "2018", "received_date": null, "accepted_date": null, "volume": "3", "issue": null, "page_first": "92", "page_last": "96", "article_number": null, "conference": { "short_name": "ISAS2018-Winter", "full_title": "2nd International Symposium on Innovative Approaches in Scientific Studies", "name": "2nd International Symposium on Innovative Approaches in Scientific Studies", "acronym": "ISAS2018-Winter", "location": "Samsun, Türkiye", "start_date": "2018-11-30", "end_date": null, "public_id": "b8a703cc-09f7-4d5b-ae60-eb31a1050720" }, "proceedings": { "title": "2nd International Symposium on Innovative Approaches in Scientific Studies", "series_title": "Setsci conference indexing system", "publisher": "SET Technology", "isbn": null, "eisbn": null, "issn": "2618-6446", "eissn": null, "doi": null, "doi_prefix": null, "slug": "setsci-volume-3-2018-legacy-39", "public_id": "641f522e-9cf0-4c89-be17-c6037f3d1bca", "url": "https://www.set-science.com/p/setsci-volume-3-2018-legacy-39" }, "license": { "name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/", "code": "CC-BY-4.0", "open_access": true }, "copyright": { "holder": "SET Technology", "year": "2018" }, "funding_statement": null, "funders": [], "references": [ { "order": 1, "raw": "1] C. Pohl, and J. van Genderen, Remote sensing image fusion: A practical guide, Crc Press, 2016.\r\n[2] W. A. Hallada, and S. Cox, “Image sharpening for mixed spatial and spectral resolution satellite systems,” in Proc. 17th International\r\nSymposium on Remote Sensing of Environment, 1983, pp. 1023-1032.\r\n[3] D. Pradines, “Improving SPOT images size and multispectral resolution,” in Earth Remote Sensing Using the Landsat Thermatic Mapper and SPOT Sensor Systems, International Society for Optics and Photonics, 1986, pp. 98-103.\r\n[4] C. K. Munechika, J. S. Warnick, C. Salvaggio, and J. R. Schott, “Resolution enhancement of multispectral image data to improve classification accuracy,” Photogramm Eng. Remote Sens., vol. 59, no. 1, pp. 67-72, 1993.\r\n[5] P. S. Chavez, and A. Y. Kwarteng, “Extracting spectral contrast in Landsat Thematic Mapper 23 image data using selective principal component analysis,” Photogramm. Eng. Remote Sens., vol. 55, pp. 339–348, 1989.\r\n[6] C. A. Laben, and B. 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Holland, Adaptation in Natural and Artificial Systems, Ann Arbor: The University of Michigan Press, 1975.\r\n[12] M. Yu, “Image segmentation using genetic algorithm and morphological operations,” Master’s Thesis, Iowa State University, 1998.\r\n[13] K. S. Tang, K .F. Man, S. Kwong, and Q. He, “Genetic algorithms and their applications,” IEEE Signal Process. Mag., vol. 13, no. 6, pp. 22- 37, 1996.\r\n[14] L. Wald, “Fusion of Images of Different Spatial Resolutions,” Presses de l'Ecole, Ecole des Mines de Paris France, 2002.\r\n[15] Z. Wang and Q. Li, “Information content weighting for perceptual image quality assessment,” IEEE Trans. Image Process., vol. 20, pp. 1185–1198, 2011.\r\n[16] Z. Wang, and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett., vol. 9, pp. 81–84, 2002.\r\n[17] M. B. Haghighat, A. A. Aghagolzadeh, and H. Seyedarabi, “A NonReference Image Fusion Metric Based on Mutual Information of Image Features,” Comput. Electr. Eng., vol. 37, no. 5, pp. 744-756, 2011.\r\n[18] A. Liu, W. Lin, and M. Narwaria, “Image quality assessment based on gradient similarity,” IEEE Trans. Image Process., vol. 21, pp. 1500– 1512, 2012.", "doi": null, "title": null, "authors": null, "source": null, "year": null, "volume": null, "pages": null } ], "statements": { "acknowledgements": null, "conflict_of_interest": null, "data_availability": null, "ai_disclosure": null }, "credit": [], "history": { "published": "2018-12-31" } }