Image fusion—the ARSIS concept and some successful implementation schemes
ISPRS Journal of Photogrammetry and Remote Sensing, ISSN: 0924-2716, Vol: 58, Issue: 1, Page: 4-18
2003
- 338Citations
- 100Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
This article aims at explaining the ARSIS concept. By fusing two sets of images A and B, one with a high spatial resolution, the other with a low spatial resolution and different spectral bands, the ARSIS concept permits to synthesise the dataset B at the resolution of A that is as close as possible to reality. It is based on the assumption that the missing information is linked to the high frequencies in the sets A and B. It searches a relationship between the high frequencies in the multispectral set B and the set A and models this relationship. The general problem for the synthesis is presented first. The general properties of the fused product are given. Then, the ARSIS concept is discussed. The general scheme for the implementation of a method belonging to this concept is presented. Then, this article intends to help practitioners and researchers to better understand this concept through practical details about implementations. Two Multiscale Models are described as well as two Inter-Band Structure Models (IBSM). They are applied to an Ikonos image as an illustration case. The fused products are assessed by means of a known protocol comprising a series of qualitative and quantitative tests. The products are found of satisfactory quality. This case illustrates the differences existing between the various models, their advantages and limits. Tracks for future improvements are discussed.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0924271603000133; http://dx.doi.org/10.1016/s0924-2716(03)00013-3; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=0038582537&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0924271603000133; https://api.elsevier.com/content/article/PII:S0924271603000133?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S0924271603000133?httpAccept=text/plain; http://linkinghub.elsevier.com/retrieve/pii/S0924271603000133; http://api.elsevier.com/content/article/PII:S0924271603000133?httpAccept=text/xml; http://api.elsevier.com/content/article/PII:S0924271603000133?httpAccept=text/plain; http://dx.doi.org/10.1016/s0924-2716%2803%2900013-3; https://dx.doi.org/10.1016/s0924-2716%2803%2900013-3
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know