An Open Image Resizing Framework for Remote Sensing Applications and Beyond
Remote Sensing, ISSN: 2072-4292, Vol: 15, Issue: 16
2023
- 4Captures
- 1Mentions
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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.
Metrics Details
- Captures4
- Readers4
- Mentions1
- Blog Mentions1
- Blog1
Article Description
Image resizing (IR) has a crucial role in remote sensing (RS), since an image’s level of detail depends on the spatial resolution of the acquisition sensor; its design limitations; and other factors such as (a) the weather conditions, (b) the lighting, and (c) the distance between the satellite platform and the ground targets. In this paper, we assessed some recent IR methods for RS applications (RSAs) by proposing a useful open framework to study, develop, and compare them. The proposed framework could manage any kind of color image and was instantiated as a Matlab package made freely available on Github. Here, we employed it to perform extensive experiments across multiple public RS image datasets and two new datasets included in the framework to evaluate, qualitatively and quantitatively, the performance of each method in terms of image quality and statistical measures.
Bibliographic Details
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