Radiometric Quality Assessment for Maxar Hd Imagery

Yalcin, I.; Kocaman, Sultan; Saunier, Sébastien; Albinet, C.

doi:10.5194/isprs-archives-xliii-b3-2021-797-2021

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…In the future, we will focus on improving our simulation model to ensure that our model is robust to the full range of the images that can be imaged by the PNeo satellites. We will also explore a full radiometric image quality assessment (Yalçin et al, 2021) for the generated super-resolved products as well as improvements to the approach, such as implementing a more progressive image restoration pipeline by separating the denoising and the zoom steps with several networks chained together and learned sequentially -and designing a hard example mining procedure for the SR problem to be able to focus on more difficult examples during training as not all patches are equals for the SR problem.…”

Section: Discussionmentioning

confidence: 99%

Joint Super-Resolution and Image Restoration for Pléiades Neo Imagery

Chouteau

Gabet

Fraisse

et al. 2022

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

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Abstract. Modern Earth Observation optical satellite systems, such as Airbus’s Pleiades Neo (PNeo) push the boundaries of high spatial resolution by providing commercial imagery products with up to 30cm ground sampling distance (GSD). To further enhance the quality of the images, the in-space imaging system is usually complemented by on-ground image restoration processing, such as deconvolution and denoising (Latry et al., 2012). Recent advances leverage Convolutional Neural Networks (CNNs) to improve the image restoration quality (K. Zhang et al., 2021a).Single Image Super-Resolution (SISR), or Zoom, the process of obtaining a higher resolution (HR) image from a lower resolved (LR) source, has recently gained traction for both medium resolution sensors such as Sentinel 2 (Lanaras et al., 2018) and high resolution such as Pléiades and GeoEye-1 (Zhu et al., 2020). This process further enhances the resolution of the image to improve downstream applications such as mapping (L. Zhang et al., 2021) and small objects recognition (Shermeyer and Van Etten, 2019). While SISR for remote sensing has been successfully tackled using CNNs (Rohith and Kumar, 2021) the main challenge for reaching acceptable image quality performance lies in the generation of realistic LR/HR training pairs (K. Zhang et al., 2021b). In this paper, we propose:a dedicated simulation chain leveraging extremely-high-resolution (EHR) aerial imagery to generate realistic 30cm Pléiades Neo images and their corresponding fully restored HR equivalent at 15cm GSDA residual-based CNN architecture which we train to jointly restore and zoom the images All contributions are assessed on real PNEO images.We deployed the trained models in a production context, to enhance the full Pléiades Neo products – with a swath of 47k pixels – in an efficient and scalable manner.

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Section: Discussionmentioning

confidence: 99%

Joint Super-Resolution and Image Restoration for Pléiades Neo Imagery

Chouteau

Gabet

Fraisse

et al. 2022

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

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“…As defined by Leachtenauer et al [34], the three main ones are scale represented in GSD, sharpness assessed from the Modulation Transfer Function (MTF), and noise level as evaluated by the Signal-to-Noise Ratio (SNR). As part of MTF analysis, Point Spread Function (PSF), Line Spread Function (LSF), Edge Spread Function (ESF), and Relative Edge Response (RER) are used to interpret the image sharpness [35]. On the other hand, six factors were taken into account by Valenzuela and Reyes [36]: the GSD, the Rayleigh diffraction limit, the ground spot size, the modified Rayleigh resolution criterion, the Sparrow limit, and the Full-Width at Half Maximum (FWHM) of the PSF/LSF for the GQA of sensors, systems, and products.…”

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confidence: 99%

Contribution of Photogrammetry for Geometric Quality Assessment of Satellite Data for Global Climate Monitoring

Kocaman,

Seiz

2023

Remote Sensing

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This article reviews the role that photogrammetry plays in evaluating the geometric quality of satellite products in connection to the long-term monitoring of essential climate variables (ECVs). The Global Climate Observing System (GCOS) is responsible for defining the observations required for climate monitoring. Only satellite products are capable of providing high-quality observations of a particular subset of ECVs on a global scale. Geometric calibration and validation of these products are crucial for ensuring the coherence of data obtained across platforms and sensors and reliable monitoring in the long term. Here, we analyzed the GCOS implementation plan and the data quality requirements and explored various geometric quality aspects, such as internal and external accuracy and band-to-band registration assessment, for a number of satellite sensors commonly used for climate monitoring. Both geostationary (GEO) and low-earth orbit (LEO) sensors with resolutions between 250 m and 3 km were evaluated for this purpose. The article highlights that the geometric quality issues vary with the sensor, and regular monitoring of data quality and tuning of calibration parameters are essential for identifying and reducing the uncertainty in the derived climate observations.

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“…Sample ESF, LSF, ESF, and MTF graphs obtained from the analysis are presented in Figure 5. Further details on the application of the approach can be found in [32,35,36].…”

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confidence: 99%

“…Sample ESF, LSF, ESF, and MTF graphs obtained from the analysis are presented in Figure 5. Further details on the application of the approach can be found in [32,35,36]. On the other hand, the SNR was also computed using the MTF target at the Salon de Provence test site.…”

mentioning

confidence: 99%

SkySat Data Quality Assessment within the EDAP Framework

Saunier¹,

Karakas

Yalcin

et al. 2022

Remote Sensing

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Cal/Val activities within the Earthnet Data Assessment Pilot (EDAP) Project of the European Space Agency (ESA) cover several Earth Observation (EO) satellite sensors, including Third-Party Missions (TPMs). As part of the validation studies of very-high-resolution (VHR) sensor data, the geometric and radiometric quality of the images and the mission compliance of the SkySat satellites owned by Planet were evaluated in this study. The SkySat constellation provides optical images with a nominal spatial resolution of 50 cm, and has the capacity for multiple visits of any place on Earth each day. The evaluations performed over several test sites for the purpose of the EDAP Maturity Matrix generation show that the high resolution requirement is fulfilled with high geometric accuracy, although various systematic and random errors could be observed. The 2D and 3D information extracted from SkySat data conform to the quality expectations for the given resolution, although improvements to the vendor-provided rational polynomial coefficients (RPCs) are essential. The results show that the SkySat constellation is compliant with the specifications and the accuracy results are within the ranges claimed by the vendor. The signal-to-noise ratio assessments revealed that the quality is high, but variations occur between the different sensors.

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Radiometric Quality Assessment for Maxar Hd Imagery

Cited by 6 publications

References 11 publications

Joint Super-Resolution and Image Restoration for Pléiades Neo Imagery

Joint Super-Resolution and Image Restoration for Pléiades Neo Imagery

Contribution of Photogrammetry for Geometric Quality Assessment of Satellite Data for Global Climate Monitoring

SkySat Data Quality Assessment within the EDAP Framework

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