A Generic Pushbroom Sensor Model for Planetary Photogrammetry

Geng, Xiurui; Xu, Qing; Xing, Shu; Lan, Chaozhen

doi:10.1029/2019ea001014

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…Acquisition: LRO NAC is a linear array pushbroom sensor with dual-view non-stereoscopic coverage, and its images are characterized by large amount of data and relatively complex camera model (Geng et al, 2020). In addition, the image matching is extremely difficult due to the rugged terrain and drastic changes in illumination at the LSP.…”

Section: Image Matching and Elevation Control Pointsmentioning

confidence: 99%

LOLA DEM Assisted Photogrammetric Processing of Lunar Reconnaissance Orbiter NAC Images for the Lunar South Pole

Liu,

Geng,

Wang

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Because of its special significance, exploration missions and research at the Lunar South Pole (LSP) are of considerable importance. The mapping products of the LSP such as Digital Ortho Maps (DOMs) and Digital Elevation Models (DEMs) are essential to support exploration missions. However, in the LSP region, the available data are relatively limited. The large amounts of shadows and the special environment of the LSP has an important impact on both image matching and generation of mapping products. In this paper, we propose a photogrammetric processing method to improve the accuracy of the LRO NAC images by using LOLA DEM as auxiliary control data. The LOLA DEM was first rendered using the same illumination conditions as the corresponding LRO NAC raw images, and then matched to the LRO NAC DOMs. Next, the corresponding elevations of the tie points between LOLA DEMs and LRO NAC images were extracted and automatically added to the control network for bundle adjustment. In order to verify the feasibility of the method, we selected 17 LRO NAC images near the LSP at 85° south latitude. The results show that the proposed method can efficiently acquire uniformly distributed elevation control points for bundle adjustment and the generated DEM has a good consistency with the LOLA DEM.

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Section: Image Matching and Elevation Control Pointsmentioning

confidence: 99%

LOLA DEM Assisted Photogrammetric Processing of Lunar Reconnaissance Orbiter NAC Images for the Lunar South Pole

Liu,

Geng,

Wang

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Thus, to improve the computational efficiency of DOM generation for LRO NAC images of the LSP, we adopted an improved back-projection algorithm for pushbroom images, and it was able to significantly decrease the number of iterations needed to determine the best scan line corresponding to a ground point [34]. Based on the generic pushbroom model of USGS ISIS, the interior orientation parameters and EO parameters of LRO NAC images were acquired and stored in a camera file (.cam) and orientation data file (.odf), respectively, according to the file format designed for the airborne linear array camera ADS40 [35]. The orthorectification process was conducted using an inverse method, and the bilinear interpolation method was used to conduct image resampling.…”

Section: Dom Generationmentioning

confidence: 99%

The Generation of High-Resolution Mapping Products for the Lunar South Pole Using Photogrammetry and Photoclinometry

Liu,

Geng,

et al. 2024

Remote Sensing

Self Cite

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High-resolution and high-accuracy mapping products of the Lunar South Pole (LSP) will play a vital role in future lunar exploration missions. Existing lunar global mapping products cannot meet the needs of engineering tasks, such as landing site selection and rover trajectory planning, at the LSP. The Lunar Reconnaissance Orbiter (LRO)’s narrow-angle camera (NAC) can acquire submeter images and has returned a large amount of data covering the LSP. In this study, we combine stereo-photogrammetry and photoclinometry to generate high-resolution digital orthophoto maps (DOMs) and digital elevation models (DEMs) using LRO NAC images for a candidate landing site at the LSP. The special illumination and landscape characteristics of the LSP make the derivation of high-accuracy mapping products from orbiter images extremely difficult. We proposed an easy-to-implement shadow recognition and contrast stretching method based on the histograms of the LRO NAC images, which is beneficial for photogrammetric and photoclinometry processing. In order to automatically generate tie points, we designed an image matching method considering LRO NAC images’ features of long strips and large data volumes. The terrain and smoothness constraints were introduced into the cost function of photoclinometry adjustment, excluding pixels in shadow areas. We used 61 LRO NAC images to generate mapping products covering an area of 400 km2. The spatial resolution of the generated DOMs was 1 m/pixel, and the grid spacing of the derived DEMs was 1 m (close to the spatial resolution of the original images). The generated DOMs achieved a relative accuracy of better than 1 pixel. The geometric accuracy of the DEM derived from photoclinometry was consistent with the lunar orbiter laser altimeter (LOLA) DEM with a root mean square error of 0.97 m and an average error of 0.17 m.

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“…where (x d ,y d ) is the distorted image coordinate and can be converted to the corrected focal plane position by the optical parameters of sensor, boresight, and pixel size can be founded in IK and (s,l) is the sample and line of image. Otherwise, the interior orientations in some sensors were conducted using affine transformation parameters given in the IK kernel [200]. However, the CK and spacecraft, orbiter and planet/satellite (SPK) kernels constructed from the actual orientation telemetry of the spacecraft or pre-measurements have intrinsic errors due to poor radio tracking accuracy, resulting in improperly positioned photogrammetric products.…”

Section: Ckmentioning

confidence: 99%

Remote Sensing and Data Analyses on Planetary Topography

2023

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Planetary mapping product established by topographic remote sensing is one of the most significant achievements of contemporary technology. Modern planetary remote sensing technology now measures the topography of familiar solid planets/satellites such as Mars and the Moon with sub-meter precision, and its applications extend to the Kuiper Belt of the Solar System. However, due to a lack of fundamental knowledge of planetary remote sensing technology, the general public and even the scientific community often misunderstand these astounding accomplishments. Because of this technical gap, the information that reaches the public is sometimes misleading and makes it difficult for the scientific community to effectively respond to and address this misinformation. Furthermore, the potential for incorrect interpretation of the scientific analysis might increase as planetary research itself increasingly relies on publicly accessible tools and data without a sufficient understanding of the underlying technology. This review intends to provide the research community and personnel involved in planetary geologic and geomorphic studies with the technical foundation of planetary topographic remote sensing. To achieve this, we reviewed the scientific results established over centuries for the topography of each planet/satellite in the Solar System and concisely presented their technical bases. To bridge the interdisciplinary gap in planetary science research, a special emphasis was placed on providing photogrammetric techniques, a key component of remote sensing of planetary topographic remote sensing.

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A Generic Pushbroom Sensor Model for Planetary Photogrammetry

Cited by 9 publications

References 28 publications

LOLA DEM Assisted Photogrammetric Processing of Lunar Reconnaissance Orbiter NAC Images for the Lunar South Pole

LOLA DEM Assisted Photogrammetric Processing of Lunar Reconnaissance Orbiter NAC Images for the Lunar South Pole

The Generation of High-Resolution Mapping Products for the Lunar South Pole Using Photogrammetry and Photoclinometry

Remote Sensing and Data Analyses on Planetary Topography

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