Photogrammetric Processing of Apollo 15 Metric Camera Oblique  Images

Edmundson, K. L.; Alexandrov, Oleg; Archinal, B. A.; Becker, K. J.; Becker, T. L.; Kirk, Randolph L.; Moratto, Z. M.; Nefian, Ara; Richie, J.; Robinson, M. S.

doi:10.5194/isprs-archives-xli-b4-375-2016

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…Therefore, as an example, consider a spacecraft placed randomly (with a uniform distribution) around the lunar sphere, thus creating a situation where we randomly image with equal probability any part of the lunar globe. We assume a camera with a full field-of-view (FOV) of about 73.7 × 73.7 deg (same as the Apollo metric camera [38,143]) and with a 2, 200 × 2, 200 pixel focal plane array (creating a 4.8 MPixel image).…”

Section: Numerical Resultsmentioning

confidence: 99%

Lunar Crater Identification in Digital Images

2021

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It is often necessary to identify a pattern of observed craters in a single image of the lunar surface and without any prior knowledge of the camera’s location. This so-called “lost-in-space” crater identification problem is common in both crater-based terrain relative navigation (TRN) and in automatic registration of scientific imagery. Past work on crater identification has largely been based on heuristic schemes, with poor performance outside of a narrowly defined operating regime (e.g., nadir pointing images, small search areas). This work provides the first mathematically rigorous treatment of the general crater identification problem. It is shown when it is (and when it is not) possible to recognize a pattern of elliptical crater rims in an image formed by perspective projection. For the cases when it is possible to recognize a pattern, descriptors are developed using invariant theory that provably capture all of the viewpoint invariant information. These descriptors may be pre-computed for known crater patterns and placed in a searchable index for fast recognition. New techniques are also developed for computing pose from crater rim observations and for evaluating crater rim correspondences. These techniques are demonstrated on both synthetic and real images.

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Section: Numerical Resultsmentioning

confidence: 99%

Lunar Crater Identification in Digital Images

2021

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“…In Figure 4 we illustrate the same three subplots as above. We have rotated the sensor look vector to be 25 • off nadir in order to approximate a viewing geometry commonly seen in planetary missions, for example, Edmundson et al (2016) . Using the sampled positional perturbations, the maximum BCBF ground error magnitude is 11.4 m. This is inline with the expected sensor positional shift only errors.…”

Section: Sensor Model Qualitymentioning

confidence: 99%

Planetary Sensor Models Interoperability Using the Community Sensor Model Specification

Laura

Mapel

Hare

2020

Earth and Space Science

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This paper presents the photogrammetric foundations upon which the Community Sensor Model specification depends, describes common coordinate system and reference frame transformations that support conversion between image sensor (charge-coupled device) coordinates to some arbitrary body coordinate, and describes the U.S. Geological Survey Astrogeology Community Sensor Model implementation (https://github.com/USGS-Astrogeology/usgscsm). We present a new image support data specification that provides the position, pointing, timing, and metadata information necessary to properly locate a pixel or observations location on a body and describe a system architecture designed to explicitly identify the responsibilities of software components within a larger pipeline or analytical environment. This paper concludes with a set of experiments that illustrate positional and pointing error in the sensor location and the impact on the computed surface location.

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“…Therefore, as an example, consider a spacecraft placed randomly (with a uniform distribution) around the lunar sphere, thus creating a situation where we randomly image with equal probability any part of the lunar globe. We assume a camera with a full field-of-view (FOV) of about 73.7 × 73.7 deg (same as the Apollo metric camera [36,138]) and with a 2, 200 × 2, 200 pixel focal plane array (creating a 4.8 MPixel image).…”

Section: Sensitivity To Crater Rim Fit and Viewing Geometrymentioning

confidence: 99%

Lunar Crater Identification in Digital Images

Christian,

Derksen,

Watkins

2020

Preprint

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It is often necessary to identify a pattern of observed craters in a single image of the lunar surface and without any prior knowledge of the camera's location. This so-called "lost-in-space" crater identification problem is common in both crater-based terrain relative navigation (TRN) and in automatic registration of scientific imagery. Past work on crater identification has largely been based on heuristic schemes, with poor performance outside of a narrowly defined operating regime (e.g., nadir pointing images, small search areas). This work provides the first mathematically rigorous treatment of the general crater identification problem. It is shown when it is (and when it is not) possible to recognize a pattern of elliptical crater rims in an image formed by perspective projection. For the cases when it is possible to recognize a pattern, descriptors are developed using invariant theory that provably capture all of the viewpoint invariant information. These descriptors may be pre-computed for known crater patterns and placed in a searchable index for fast recognition. New techniques are also developed for computing pose from crater rim observations and for evaluating crater rim correspondences. These techniques are demonstrated on both synthetic and real images.

show abstract

Photogrammetric Processing of Apollo 15 Metric Camera Oblique Images

Cited by 4 publications

References 4 publications

Lunar Crater Identification in Digital Images

Lunar Crater Identification in Digital Images

Planetary Sensor Models Interoperability Using the Community Sensor Model Specification

Lunar Crater Identification in Digital Images

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