Crater Detection and Registration of Planetary Images Through Marked Point Processes, Multiscale Decomposition, and Region-Based Analysis

Solarna, David; Gotelli, Alberto; Moigne, Jacqueline Le; Moser, Gabriele

doi:10.1109/tgrs.2020.2970908

Cited by 12 publications

(4 citation statements)

References 84 publications

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“…Let us also suppose that the two images are well registered so that it is possible to process them coherently on the same reference frame. In the literature, many image registration methods exist [68][69][70], also capable of addressing multisensor registration problems [71][72][73].…”

Section: Energy Function Of the Proposed Markov Modelmentioning

confidence: 99%

Monitoring the Recovery after 2016 Hurricane Matthew in Haiti via Markovian Multitemporal Region-Based Modeling

et al. 2021

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The aim of this paper is to address the monitoring of the recovery phase in the aftermath of Hurricane Matthew (28 September–10 October 2016) in the town of Jérémie, southwestern Haiti. This is accomplished via a novel change detection method that has been formulated, in a data fusion perspective, in terms of multitemporal supervised classification. The availability of very high resolution images provided by last-generation satellite synthetic aperture radar (SAR) and optical sensors makes this analysis promising from an application perspective and simultaneously challenging from a processing viewpoint. Indeed, pursuing such a goal requires the development of novel methodologies able to exploit the large amount of detailed information provided by this type of data. To take advantage of the temporal and spatial information associated with such images, the proposed method integrates multisensor, multisource, and contextual information. Markov random field modeling is adopted here to integrate the spatial context and the temporal correlation associated with images acquired at different dates. Moreover, the adoption of a region-based approach allows for the characterization of the geometrical structures in the images through multiple segmentation maps at different scales and times. The performances of the proposed approach are evaluated on multisensor pairs of COSMO-SkyMed SAR and Pléiades optical images acquired over Jérémie, in the aftermath of and during the three years after Hurricane Matthew. The effectiveness of the change detection results is analyzed both quantitatively, through the computation of accuracy measures on a test set, and qualitatively, by visual inspection of the classification maps. The robustness of the proposed method with respect to different algorithmic choices is also assessed, and the detected changes are discussed in relation to the recovery endeavors in the area and ground-truth data collected in the field in April 2019.

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Section: Energy Function Of the Proposed Markov Modelmentioning

confidence: 99%

Monitoring the Recovery after 2016 Hurricane Matthew in Haiti via Markovian Multitemporal Region-Based Modeling

et al. 2021

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“…Thus, after a brief review of existing techniques, a novel method is proposed. There exist various ad hoc methods based on the explicit ellipse parameters (a, b, x c , y c , and ψ) [30,113] or by measuring the distance only at one specific point (e.g., via the Hausdorff distance [132,136]), though none of these are similarity invariants. Moreover, these methods have changing geometric meaning as the two ellipses change shape and relative orientation.…”

Section: Similarity Invariance: D(a I mentioning

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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“…Thus, after a brief review of existing techniques, a novel method is proposed. There exist various ad hoc methods based on the explicit ellipse parameters (a, b, x c , y c , and ψ) [108,28] or by measuring the distance only at one specific point (e.g., via the Hausdorff distance [131,127]), though none of these are similarity invariants. Moreover, these methods have changing geometric meaning as the two ellipses change shape and relative orientation.…”

Section: Triangle Inequality: D(amentioning

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

Crater Detection and Registration of Planetary Images Through Marked Point Processes, Multiscale Decomposition, and Region-Based Analysis

Cited by 12 publications

References 84 publications

Monitoring the Recovery after 2016 Hurricane Matthew in Haiti via Markovian Multitemporal Region-Based Modeling

Monitoring the Recovery after 2016 Hurricane Matthew in Haiti via Markovian Multitemporal Region-Based Modeling

Lunar Crater Identification in Digital Images

Lunar Crater Identification in Digital Images

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