Journal article
Geert J. Verhoeven
AARGnews, vol. 57, 2018, pp. 25-34
AARGnews, vol. 57, 2018, pp. 25-34
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APA
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Verhoeven, G. J. (2018). Resolving some spatial resolution issues – Part 1: Between line pairs and sampling distance. AARGnews, 57, 25–34. https://doi.org/10.5281/zenodo.1465017
Chicago/Turabian
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Verhoeven, Geert J. “Resolving Some Spatial Resolution Issues – Part 1: Between Line Pairs and Sampling Distance.” AARGnews 57 (2018): 25–34.
MLA
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Verhoeven, Geert J. “Resolving Some Spatial Resolution Issues – Part 1: Between Line Pairs and Sampling Distance.” AARGnews, vol. 57, 2018, pp. 25–34, doi:10.5281/zenodo.1465017.
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@article{verhoeven2018a,
title = {Resolving some spatial resolution issues – Part 1: Between line pairs and sampling distance},
year = {2018},
journal = {AARGnews},
pages = {25-34},
volume = {57},
doi = {10.5281/zenodo.1465017},
author = {Verhoeven, Geert J.}
}
Abstract
One of the most common words in the remote sensing (or even general imaging) literature is ‘resolution’. Despite its abundant use and because the concept is often misjudged as uncomplicated, most modern literature relies on rather sloppy ‘resolution’ definitions that sometimes even contradict each other within the same text. In part, this confusion and misconception arises from the fact that technical as well as broader, application‐specific explanations for resolution exist, both of them even relying on different ways to describe resolution characteristics. As a result, the term ‘resolution’ has been used for many years as a handy go‐to term to cover many concepts: “this satellite produces images with a resolution of 30 m”; “there is an increasing number of high‐resolution camera sensors on the market” or “the resolution of the human eye is coarser than an eagle’s eye”. Nowadays, one might wonder if resolution is a particular image characteristic, a property of the imaged scene or instead related to the imaging sensor or maybe the camera’s lens.
It is thus fair to say that the technical concept of resolution – or more specifically spatial resolution – and all its implications are commonly poorly understood, which leads to many popular, accepted but completely wrong statements. In the photographic literature, a widespread example is to refer to the total number of camera image pixels (i.e. the pixel count) as the image resolution of that specific digital camera. This is erroneous since the same 24‐megapixel camera can capture a photograph of an Attic black‐figure amphora as well as a complete submerged Greek temple. The resulting two photographs, although both are counting 24 megapixels, might reveal scene details of 0.01 cm and 2 cm respectively. In the remote sensing community, a prevalent misconception is that a satellite image with a 1 m resolution automatically means that we can recognise all objects in that image which have a width equal to or larger than 1 m.
In this two‐part entry of our series, we will combine simple geometrical relationships (part 1) and fundamental laws of electromagnetic radiation (part 2) to shed some light on the term spatial resolution and explain its difference with the related concept of spatial resolving power. Similar to the previous two entries, this two‐pieced text can only scratch the surface of this very complex topic. Notwithstanding, the aim is still to provide solid definitions and enough background knowledge to easily correct many of the “common knowledge” but ill‐founded statements such as the ones mentioned above.
One of the most common words in the remote sensing (or even general imaging) literature is ‘resolution’. Despite its abundant use and because the concept is often misjudged as uncomplicated, most modern literature relies on rather sloppy ‘resolution’ definitions that sometimes even contradict each other within the same text. In part, this confusion and misconception arises from the fact that technical as well as broader, application‐specific explanations for resolution exist, both of them even relying on different ways to describe resolution characteristics. As a result, the term ‘resolution’ has been used for many years as a handy go‐to term to cover many concepts: “this satellite produces images with a resolution of 30 m”; “there is an increasing number of high‐resolution camera sensors on the market” or “the resolution of the human eye is coarser than an eagle’s eye”. Nowadays, one might wonder if resolution is a particular image characteristic, a property of the imaged scene or instead related to the imaging sensor or maybe the camera’s lens.
It is thus fair to say that the technical concept of resolution – or more specifically spatial resolution – and all its implications are commonly poorly understood, which leads to many popular, accepted but completely wrong statements. In the photographic literature, a widespread example is to refer to the total number of camera image pixels (i.e. the pixel count) as the image resolution of that specific digital camera. This is erroneous since the same 24‐megapixel camera can capture a photograph of an Attic black‐figure amphora as well as a complete submerged Greek temple. The resulting two photographs, although both are counting 24 megapixels, might reveal scene details of 0.01 cm and 2 cm respectively. In the remote sensing community, a prevalent misconception is that a satellite image with a 1 m resolution automatically means that we can recognise all objects in that image which have a width equal to or larger than 1 m.
In this two‐part entry of our series, we will combine simple geometrical relationships (part 1) and fundamental laws of electromagnetic radiation (part 2) to shed some light on the term spatial resolution and explain its difference with the related concept of spatial resolving power. Similar to the previous two entries, this two‐pieced text can only scratch the surface of this very complex topic. Notwithstanding, the aim is still to provide solid definitions and enough background knowledge to easily correct many of the “common knowledge” but ill‐founded statements such as the ones mentioned above.
