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) |