Opin vísindi

Spatially Enhanced Spectral Unmixing Through Data Fusion of Spectral and Visible Images from Different Sensors

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dc.contributor Háskóli Íslands
dc.contributor University of Iceland
dc.contributor.author Kizel, Fadi
dc.contributor.author Benediktsson, Jon Atli
dc.date.accessioned 2021-01-15T13:43:58Z
dc.date.available 2021-01-15T13:43:58Z
dc.date.issued 2020-04-16
dc.identifier.citation Kizel F, Benediktsson JA. Spatially Enhanced Spectral Unmixing Through Data Fusion of Spectral and Visible Images from Different Sensors. Remote Sensing. 2020; 12(8):1255.
dc.identifier.issn 2072-4292
dc.identifier.uri https://hdl.handle.net/20.500.11815/2384
dc.description Publiher's version (útgefin grein)
dc.description.abstract We propose an unmixing framework for enhancing endmember fraction maps using a combination of spectral and visible images. The new method, data fusion through spatial information-aided learning (DFuSIAL), is based on a learning process for the fusion of a multispectral image of low spatial resolution and a visible RGB image of high spatial resolution. Unlike commonly used methods, DFuSIAL allows for fusing data from different sensors. To achieve this objective, we apply a learning process using automatically extracted invariant points, which are assumed to have the same land cover type in both images. First, we estimate the fraction maps of a set of endmembers for the spectral image. Then, we train a spatial-features aided neural network (SFFAN) to learn the relationship between the fractions, the visible bands, and rotation-invariant spatial features for learning (RISFLs) that we extract from the RGB image. Our experiments show that the proposed DFuSIAL method obtains fraction maps with significantly enhanced spatial resolution and an average mean absolute error between 2% and 4% compared to the reference ground truth. Furthermore, it is shown that the proposed method is preferable to other examined state-of-the-art methods, especially when data is obtained from different instruments and in cases with missing-data pixels.
dc.description.sponsorship This research was partially funded by the Icelandic Research Fund through the EMMIRS project, and bythe Israel Science Ministry and Space Agency through the Venus project.
dc.format.extent 1255
dc.language.iso en
dc.publisher MDPI AG
dc.relation.ispartofseries Remote Sensing;12(8)
dc.rights info:eu-repo/semantics/openAccess
dc.subject Data fusion
dc.subject Multispectral images
dc.subject Remote sensing
dc.subject Spatial information
dc.subject Spatial resolution
dc.subject Spectral unmixing
dc.subject Fjarkönnun
dc.subject Myndgreining (upplýsingatækni)
dc.title Spatially Enhanced Spectral Unmixing Through Data Fusion of Spectral and Visible Images from Different Sensors
dc.type info:eu-repo/semantics/article
dcterms.license This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
dc.description.version Peer Reviewed
dc.identifier.journal Remote Sensing
dc.identifier.doi 10.3390/RS12081255
dc.relation.url https://www.mdpi.com/2072-4292/12/8/1255/pdf
dc.contributor.department Rafmagns- og tölvuverkfræðideild (HÍ)
dc.contributor.department Faculty of Electrical and Computer Engineering (UI)
dc.contributor.school Verkfræði- og náttúruvísindasvið (HÍ)
dc.contributor.school School of Engineering and Natural Sciences (UI)

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