Satellite image data concepts - Revision history

Bmueller: /* Retroreflection and multiple reflection */

2022-07-31T09:25:31Z

Retroreflection and multiple reflection

Bmueller: /* Modalities */

2022-07-29T19:46:29Z

Modalities

Bmueller at 17:23, 22 July 2022

2022-07-22T17:23:45Z

Vruba: /* Processing levels */

2022-07-21T01:58:55Z

Processing levels

Vruba: /* Georeferencing and orthorectification */ one inline ref done, and only 11,324 to go

2022-07-21T01:57:40Z

Georeferencing and orthorectification: one inline ref done, and only 11,324 to go

Vruba: /* DN and PN */ clarification

2022-07-21T01:22:17Z

DN and PN: clarification

Vruba: /* Formats and projections */ I’m so drunk right now

2022-07-21T01:19:57Z

Formats and projections: I’m so drunk right now

Vruba: /* Formats and projections */ various rephrasings and clarifications

2022-07-21T01:17:54Z

Formats and projections: various rephrasings and clarifications

Vruba: /* Sar resolves space with time, not with focus */

2022-07-21T00:49:51Z

Sar resolves space with time, not with focus

Vruba: /* Formats and projections */

2022-07-20T23:20:46Z

Formats and projections

← Older revision		Revision as of 09:25, 31 July 2022
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	Where the reflection is separated enough from the first reflecting surface that you can see both independently, we use the term multiple reflection (or mirroring or ghosting). This most often happens where tall buildings or bridges are next to or over water. A radio wave may hit the water, then a bridge, then return to the sensor; another may hit a bridge, then the water, and return to the sensor, and so on, and you’ll see images of multiple bridges.		Where the reflection is separated enough from the first reflecting surface that you can see both independently, we use the term multiple reflection (or mirroring or ghosting). This most often happens where tall buildings or bridges are next to or over water. A radio wave may hit the water, then a bridge, then return to the sensor; another may hit a bridge, then the water, and return to the sensor, and so on, and you’ll see images of multiple bridges.
		+	[[File:Benjamin Pittet - Russia has set up a pontoon bridge next to the railway bridge in Kherson. 46.675185, 32.797150-1552918575098331137.mp4\|center\|thumb\|Is the outlined section a pontoon bridge, or just distortion? ([https://twitter.com/SteveDLF/status/1553168045849186304?s=20&t=K_9eOaNnxSijx2dTXV7kKg Answer])]]

	==== Layover and shadowing ====		==== Layover and shadowing ====

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 == Modalities ==
-A sensor’s modality is the form of energy it senses and the general principles it uses to construct useful data. For example, microphones are sensors whose modality is measuring air pressure to record sound, barometers are sensors whose modality is using air pressure to record weather-scale atmospheric events, and everyday cameras are sensors whose modality is measuring visible light to record focused images.
+A sensor’s modality is the form of energy it senses and the general principles it uses to construct useful data. For example, microphones are sensors whose modality is measuring air pressure to record sound, barometers are sensors whose modality is using air pressure to record weather-scale atmospheric events, and everyday cameras are sensors whose modality is measuring visible light to record focused images. The two most common modalities are Optical Imagery (passive) and Synthetic Aperture Radar (SAR, active), though others such as LIDAR also exist.
 Here we’ll define the optical domain as anything transmitted by Earth’s atmosphere in [https://en.wikipedia.org/wiki/Atmospheric_window#/media/File:Atmospheric_Transmission.svg the windows] between about 300 nm and 3 μm. This includes near ultraviolet (here, “near” means “near visible”, not “almost”), visible, near infrared, and shortwave infrared light, but not thermal infrared. You might also see this range described as, for example, VNIR + SWIR – visible, near infrared, and shortwave infrared. We’ll use Landsat as an example again, since its OLI sensor (on Landsat 8 and 9) is well-known and fairly typical of rich multispectral sensors. Its bands are:
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 # Full-frame
-=== Thermal ===
+== Thermal ==
 ''This section is a stub. Please start it!''
-=== Synthetic aperture radar ===
+== Synthetic aperture radar ==
 Synthetic aperture radar, or SAR, creates images with radio waves in wavelengths around 1 cm to 1 m.
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 When inSAR works, it’s like magic. You can pick up extremely subtle effects over large areas. It does have limits, like that you can only measure displacement towards or away from the satellite(s), which for SAR is always at least somewhat to the side, which is not necessarily in the direction you actually care about (say, up/down). And as you would expect, it tends to require a lot of very good data (because, for example, satellite orbits are never absolutely perfect repeats), expertise, and minutes to days of fine-tuning.
-=== LIDAR ===
+== LIDAR ==
 ''This section is a stub. Please start it!''

← Older revision		Revision as of 17:23, 22 July 2022
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	For most OSINT-relevant analysis, working in DN is a [https://en.wikipedia.org/wiki/Venial_sin venial sin] at worst and often justifiable. But it is useful to know what it means and to recognize situations where you should convert to PN. Any tool designed to work with remote sensing data will at least have some affordance for DN to PN scaling, and, again, may be able to parse the parameters out of a bundle (or in-image-file metadata) and apply them transparently so you never have to think about it.		For most OSINT-relevant analysis, working in DN is a [https://en.wikipedia.org/wiki/Venial_sin venial sin] at worst and often justifiable. But it is useful to know what it means and to recognize situations where you should convert to PN. Any tool designed to work with remote sensing data will at least have some affordance for DN to PN scaling, and, again, may be able to parse the parameters out of a bundle (or in-image-file metadata) and apply them transparently so you never have to think about it.
		+
		+	== References ==

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 === Processing levels ===
-In theory, data processing levels are standard across the industry. In practice, different providers tend to make up their own definitions as necessary, and you should refer to source-specific documentation. But typically, the commonly seen processing levels are:
+In theory, data processing levels are standard across the industry. In practice, different providers tend to make up their own definitions as necessary, and you should refer to source-specific documentation. But as a sketch, processing levels are something like:
 '''Level 0''': Unprocessed data, more or less as downlinked to the ground station. Generally not sold or publicly released.

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 * Earth’s surface is rough at every scale, and even “porous” or multiply defined in the sense that there are features like leafless trees that make it hard to define where the optical surface actually ''is'' at any given scale.
-* There is no perfectly [https://en.wikipedia.org/wiki/Accuracy_and_precision accurate, precice], global, completely up-to-date terrain model of the Earth, let alone at a reasonable price. SRTM is pretty good but it’s only about 30 m, stops short of the arctic, and is 20+ years out of date: there are entire lakes, highway cuts, and reclaimed islands that don’t exist in it.
+* There is no perfectly accurate, precise,<ref>https://en.wikipedia.org/wiki/Accuracy_and_precision</ref> global, completely up-to-date terrain model of the Earth, let alone at a reasonable price. SRTM is pretty good but it’s only about 30 m, stops short of the arctic, and is 20+ years out of date: there are entire lakes, highway cuts, and reclaimed islands that don’t exist in it.
 * Satellites typically only know where they’re pointing to within the equivalent of about 10 pixels (which, to be fair, is usually an extremely small fraction of a degree), so the pointing data can only narrow things down, not actually tell you where you are.
 * Continental drift means that a continent can move by easily 1 px over the lifetime of a high-end commercial satellite; a major earthquake can discontinuously distort a small region by several m.

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 Image formats generally store integers, since they losslessly compress better and are often easier to work with than floating point numbers. However, this presents a problem if, for example, the units being represented are reflectance, which ranges from 0 to 1. If we round every reflectance value to either 0 or 1, we’re delivering 1-bit data that’s probably close to totally useless. To address this, we might scale up to, say, 0 through 100 and say that instead of recording reflectance fraction, we’re recording reflectance percentage – fraction × 100. That still leaves us with less than 7 bits of radiometric resolution, though. Really, we’d like to be able to scale our values into an arbitrary range, maybe 0 through 65,535 to make full use of a 16-bit image, and send it with some metadata that tells how to get it back into some absolute or physically meaningful unit. You could even change the scaling factor per scene to optimize for bright v. dark, for example. And this is what providers generally do. The values actually stored in the image format are called digital numbers, or DN, and the values after scaling (typically with a multiplicative and an additive coefficient) are physical numbers, or PN.
-Not all providers do this. For example, Sentinel-2 level 1C data has a globally constant scaling factor, which means different bands have a defined relationship even if you read raw, unscaled pixels out of them, which is great. However, it’s the most common approach. Basically, don’t assume that pixels actually mean anything with an absolute definition, especially compared to pixels from another band or scene, unless you know that they’re PN.
+Not all providers do this. For example, Sentinel-2 level 1C data has a globally constant scaling factor, which means different bands have a defined relationship even if you read raw, unscaled pixels out of them, which is great. However, custom coefficients is the most common approach. Basically, don’t assume that pixels actually mean anything with an absolute definition, especially compared to pixels from another band or scene, unless you know that they’re PN.
 For most OSINT-relevant analysis, working in DN is a [https://en.wikipedia.org/wiki/Venial_sin venial sin] at worst and often justifiable. But it is useful to know what it means and to recognize situations where you should convert to PN. Any tool designed to work with remote sensing data will at least have some affordance for DN to PN scaling, and, again, may be able to parse the parameters out of a bundle (or in-image-file metadata) and apply them transparently so you never have to think about it.

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 === Formats and projections ===
-Bands are just 2D or arrays of numbers. A typical RGB image from a phone, for example, can be defined as a stack of 3 × 2D arrays of brightness values, one each for red, green, and blue. In virtually all cases, a larger number means more energy was detected at the point represented by that pixel.
+Bands are just 2D arrays of numbers. A typical RGB image from a phone, for example, can be defined as a stack of 3 × 2D arrays of brightness values, one each for red, green, and blue. In virtually all cases, a larger number means more energy was detected at the point represented by that pixel.
 Satellite image data usually comes in formats optimized for large payloads and good metadata. These include HDF, NetCDF, NITF, JPEG2000, TIFF, and GeoTIFF. The GDAL library, which is included in QGIS and has command-line tools, can read virtually any reasonable format. If you have a choice, GeoTIFF is a good option – it’s an open standard<ref>http://docs.opengeospatial.org/is/19-008r4/19-008r4.html</ref> that defines a way of using TIFF metadata to encode geolocation.

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 === Formats and projections ===
 Probably the most common projection you will see for raw data is Universal Transverse Mercator, or UTM. It’s actually a family of projections with the same formula but different parameters, each adapted to a different meridional slice of Earth’s surface. These UTM zones are named with numbers and north/south hemispheres: Paris is in UTM zone 31N, Geneva is in 32N, and Sydney is in 56S. (If you’ve used the MGRS grid system, this should sound familiar, but it’s not identical.) Within a zone, UTM is very close to equal-area and conformal, which are the most important properties for a projection if you want to do analytical work. Equal-area means 1 km² is the same number of pixels at any point in the projection, and conformal means that 1 km is the same number of pixels in every direction from any given point within the projection. (On a non-conformal map, circles appear as ovals, squares are rectangles, etc. This is a massive pain in the ass.) UTM is EPSG:32XYY, where X is 6 for N and 7 for S, and YY is the zone number, so for example 13S is EPSG:32713.
-For display on standard web maps, people often use web Mercator, a.k.a. spherical Mercator, which is not equal-area at the global scale, but is conformal. This is why web maps make Greenland far too big, but it remains approximately the right shape. For local analysis, web Mercator is fine (equivalent to UTM, actually), and can be a decent choice if you understand the issues with scale across large areas. EPSG:3857.
+For display on standard web maps, people often use web Mercator, a.k.a. spherical Mercator, which is not equal-area at the global scale, but is conformal. This is why web maps make Greenland far too big, but it remains approximately the right shape. For local analysis, web Mercator is fine (essentially equivalent to UTM, actually), and can be a decent choice if you understand the issues with scale across large areas. EPSG:3857.
-The other projection you’ll see the most is equirectangular or plate carrée, which uses longitude and latitude directly as x and y coordinates on a plane. It is neither equal-area nor conformal, and basically only exists because the math is easy. It’s often used by people who should know better. Its non-conformality means that any time you’re working near the poles, everything is squeezed, and you’re either overzooming one dimension, losing data in the other, or both. If you just want to scatterplot some points as quickly as possible, equirectangular is fine, but avoid it when doing anything with imagery. EPSG:4326. (Note that this is the EPSG of WGS84, the geodetic standard that defines things like the prime meridian. Many, many other projections refer to WGS84 in their definitions. But using WGS84 as a projection itself, instead of as an ingredient in a projection, is the equirectangular projection.)
+The other projection you’ll see the most is equirectangular or plate carrée, which uses longitude and latitude directly as x and y coordinates on a plane. It is neither equal-area nor conformal, and basically only exists because the math is easy. It’s often used by people who should know better. Its non-conformality means that any time you’re working near the poles, everything is squeezed, and you’re either overzooming one dimension, losing data in the other, or both. If you just want to scatterplot some points as quickly as possible, equirectangular is fine, but avoid it when doing anything with imagery. EPSG:4326. (Note that this is the EPSG of WGS84, the geodetic standard used by GPS and which defines things like the prime meridian. Many, many other projections refer to WGS84 in their definitions. But using WGS84 as a projection itself, instead of as an ingredient in a projection, is the equirectangular projection.)
 The details of projections are notoriously tricky; it’s hard to work with them in a strictly correct and optimal way at all times. It’s the kind of topic that attracts pedantry and flamewars, unfortunately. Here’s some advice, none of it ironclad:
-# Most imagery data, if it’s projected at all, is already in an appropriate projection as it arrives from the data provider. If you can, leave it as-is. Every reprojection involves resampling the data, which generally loses information.
+# Most imagery data, if it’s projected at all, is already in a reasonable projection as it arrives from the data provider. If reasonably possible, leave it as-is. Every reprojection involves resampling the data, which generally loses information.
-# You should rarely have to explicitly think about projections. The whole point of a projection is to let you think in terms of pixels and/or meters, and if that’s not happening, something is wrong. Make sure you’re taking full advantage of your tools’ ability to handle these things automatically. Fighting your projection means something is wrong.
+# You should rarely have to explicitly think about projections. The whole point of a projection is to let you think in terms of pixels and/or meters, and if that’s not happening, something is wrong. Make sure you’re taking full advantage of your tools’ ability to handle these things automatically. Fighting your projection is a cue to take a step back and think about what you’re doing.
 # If you’re working on a multi-source project, choose a suitable projection at the start and project all data into it ''once'', when you import it.
 # Most pain around projections comes from accidentally mixing projections. Don’t do that.

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 ==== Sar resolves space with time, not with focus ====
-SAR’s resolution is based on the timing of returning signals. It does not pass the energy it senses through a focusing lens or mirror the way an optical sensor does. This leads to properties that are highly unintuitive if you think of it as merely “optical but in a different frequency” – for example, it does not loose resolution with distance; there is no exact equivalent of perspective. SAR is more like side-scan sonar than like an everyday camera.
+SAR’s spatial resolution is based on the timing of returning signals. It does not pass the energy it senses through a focusing lens or mirror the way an optical sensor does. This leads to properties that are highly unintuitive if you think of it as merely “optical but in a different frequency” – for example, it does not loose resolution with distance; there is no exact equivalent of perspective. SAR is more like side-scan sonar than like an everyday camera.
 ==== Speckle ====

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 === Formats and projections ===
-Image data generally comes in formats optimized for large payloads and good metadata. These include NetCDF, NITF, JPEG2000, and GeoTIFF. GDAL, which is included in QGIS, can read virtually any reasonable format. If you have a choice, GeoTIFF is usually the best.
+Image data generally comes in formats optimized for large payloads and good metadata. These include HDF, NetCDF, NITF, JPEG2000, and GeoTIFF. GDAL, which is included in QGIS, can read virtually any reasonable format. If you have a choice, GeoTIFF is usually the best.
 A geographic projection is basically an invertible function (a reversible, one-to-one relationship) from a sphere to a plane. (If you know enough about geodesy to be saying “the spheroid, actually” right now, go read something more appropriate to your level of expertise 😉.) In other words, for a longitude and a latitude on Earth, a given projection gives you a corresponding x and y that you use to store and display the data.