Abstract
Reproducible research is needed to ensure that scientific results in the literature are reliable, unbiased, and verifiable by others. The journal Image Processing On Line (IPOL) publishes reproducible articles since 2010. This means publishing an algorithm by a literary description, a pseudocode, its source code, a series of test examples, an online facility allowing to test the code on this data and other data submitted by the user, and finally an experimental archive. In this work, we discuss how to publish and review reproducible research in the specific discipline of remote sensing. We put a special emphasis on the construction and proper documentation of public datasets. We show case studies of remote sensing articles publicly available in IPOL, which demonstrate the feasibility of reproducible research in this area. The methods and their application are explained, along with details on how the datasets were built and made available for evaluation, comparison, and scoring to eventually help establish a reliable state-of-the-art of the discipline. Finally, we give specific recommendations for authors and editors willing to publish reproducible research in remote sensing.
Highlights
T HE credibility crisis in scientific research was warned by Donoho (2009) and other researchers [1]
In this work, based on the experience of the last ten years developping the Image Processing On Line (IPOL) journal, we give recommendations to authors and editors willing to publish reproducible research in remote sensing, and show real case studies demonstrating that reproducible research in remote sensing is certainly feasible
II-A we describe how the review process is conducted in the IPOL journal
Summary
T HE credibility crisis in scientific research was warned by Donoho (2009) and other researchers [1]. To the question Have you ever encountered difficulties in reproducing results from other researchers?, some 53% answer “yes, sometimes”, 21% “yes, often” and more surprisingly 17% answer “No, I never tried” Many initiatives such as journals devoted to fully-reproducible research, platforms, and services have started trying to address this problem. In this work, based on the experience of the last ten years developping the IPOL journal, we give recommendations to authors and editors willing to publish reproducible research in remote sensing, and show real case studies demonstrating that reproducible research in remote sensing is certainly feasible (and absolutely necessary).
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