Rigorous Sensor Model of Gaofen-7 Satellite Laser Altimeter Based on Coupled Footprint Camera

Li, Shaoning; Zhang, Guo; Fan, Xiufang

doi:10.1109/jstars.2023.3242736

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…From the perspective of the data source, the image angle of both fields in GF-2 data is 3.45°, and its deformation can be simply replaced by a quadratic polynomial in a wide-range image. However, the angles of the forward and backward cameras equipped with GF-7 are +26° and -5° respectively [50]. At this time, the deformation of the image is not limited > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < to the plane, and the quadratic polynomial method cannot completely fit its deformation.…”

Section: The Influence Of Different Mismatch Points Methods On Bamentioning

confidence: 99%

MSRSI-TPMF: A Tie Points Matching Framework of Multisource Remote Sensing Images

Cheng,

Li,

Wang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Remote sensing sensor platforms are typically located at a significant distance from the ground, ranging from several hundred meters to hundreds of kilometers. This means that, compared to natural images, remote sensing images (RSI) have larger coverage areas and more complex information. The larger size and data volume of RSI presents challenges for computer vision matching algorithms (MAs), making it difficult to apply them directly to RSI matching. Moreover, a matching framework for multi-source remote sensing images capable of large-scale processing by integrating multiple MAs with the entire RSI as input is presently lacking. This study proposes a Tie Points (TPs) Matching Framework of Multi-Source Remote Sensing Images (MSRSI-TPMF) based on the geometric and radiation characteristics of RSI. First, RSI is divided into different grids and undergo local geometry correction. Next, matching between slice images are performed by MAs. Finally, TPs are generated by mapping matched points in multiple slice images to the whole RSI using a geometric processing model. Six representative MAs including artificial feature matching algorithms and deep learning algorithms are integrated into the framework to match TPs from different RSI. Results demonstrate the extraction of TPs for multi-source RSI, validating the framework's efficacy. In addition, a large-scale TPs matching test for deep learning matching algorithm is performed by using 13 synthetic aperture radar (SAR) images (10m resolution) with TPs root mean square error (RMSE) of 0.368 pixels, further confirming the framework's reliability. Our framework is available at https://github.com/TobyChengV1/MSRSI-TPMF.

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Section: The Influence Of Different Mismatch Points Methods On Bamentioning

confidence: 99%

MSRSI-TPMF: A Tie Points Matching Framework of Multisource Remote Sensing Images

Cheng,

Li,

Wang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In 2018, the altimeter system of the Global Ecosystem Dynamics Investigation (GEDI) was installed at the International Space Station, mainly for the measurement of the vertical structure of forests [15,16]. In November 2019, the GF-7 satellite was launched with two linear system laser altimeters, which opened the exploration of full-waveform large-footprint laser altimetry in China [17][18][19][20]. Recently, the Terrestrial Ecosystem Carbon Inventory Satellite, dedicated to forest resource surveys, has been launched, carrying a five-beam lidar load for acquiring high-precision three-dimensional structures of forests at large footprints [21,22].…”

Section: Introductionmentioning

confidence: 99%

A Calibration Method for Large-Footprint Full-Waveform Airborne Laser Altimeter without a Calibration Field

et al. 2023

Remote Sensing

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The geometrical measurement precision of laser spots is affected by the deviation between the parameters of the laser altimeter and the laboratory measurement results, and the inversion accuracy of surface object height is also limited. The measurement parameters and the load state can be obtained by calibration of the laser altimeter system. Usually, ground detectors are deployed to calibrate the measurement parameters of the laser altimeter, including the divergence angle and the energy distribution of the laser beam. A calibration method for a laser footprint spot without a calibration field was proposed in this paper, focused on the airborne large-footprint laser altimeter system. The geometric parameters of the laser spot were calibrated through the laser echo waveforms of a specific terrain. The experimental results show that geometric calibration of the large-footprint laser altimeter can be achieved in the area of the step surface. The divergence angle of the laser beams obtained from the six experimental areas is 4.604 ± 0.359 mRad, and the consistency of the energy distribution from each laser spot reaches 92.67%. A new method of on-orbit calibration and verification is provided for the satellite laser altimeter system.

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First experience of 3D georeferencing accuracy assessment of SPOT 6 stereo panchromatic primary images by sensor-dependent orientation model

Topan

2023

Remote Sensing Letters

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Rigorous Sensor Model of Gaofen-7 Satellite Laser Altimeter Based on Coupled Footprint Camera

Cited by 3 publications

References 19 publications

MSRSI-TPMF: A Tie Points Matching Framework of Multisource Remote Sensing Images

MSRSI-TPMF: A Tie Points Matching Framework of Multisource Remote Sensing Images

A Calibration Method for Large-Footprint Full-Waveform Airborne Laser Altimeter without a Calibration Field

First experience of 3D georeferencing accuracy assessment of SPOT 6 stereo panchromatic primary images by sensor-dependent orientation model

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