ABSTRACT:The Pleiades system, ORFEO system optical component (Optical and Radar Federated Earth Observation) consists of a constellation of two satellites for very High Resolution panchromatic and multispectral optical observation of the Earth. Its mission is to cover all European civilian needs (mapping, tracking floods and fires) and defence in the category of metric resolution: 0.7m Nadir. The first Pleiades satellite was launched at the end of last year. One of the key objectives of the Pleiades HR (PHR) project is to achieve a location accuracy that will allow the use of images in GIS (Geographical Information System) without geometrical model improvement by refining on ground control points. The image location without refined model was specified with the precision of the most commonly used tool ie the civil GPS. So the location accuracy has been specified at less than 12m for 90% of the images on a nominal satellite configuration. Very special care has been taken all along the PHR project realization to achieve this very good location accuracy. The final touch is given during the in-orbit commissioning phase which lasts until June 2012. The geometric quality implies to tune the parameters involved in the geolocation model (geometric calibration): besides attitude and orbit restitution tuning (not considered here), it consists in estimating the biases between the instrument orientation and the AOCS reference frame, and also the sight line of each detector in the focal plane. This is called static geometrical model. The analysis of dynamic perturbations outside of the model are the second most important image quality objective of in-flight commissioning, not described in this paper. Finally "image quality assessment" consists in evaluating the image quality obtained in the final products. For geolocation model, it is quantified by the absolute geolocation and the pointing accuracies, and it is a main contributor in length alteration and planimetric and altimetric accuracies. In this paper we will present both the different practices we have adopted (their advantages, limitations and complementarities) and the means we are using for the operational assessment of the location quality of PHR images. We will focus on the innovative methods and mention the improvements in progress. To conclude, we will present the very first accuracy results assessed after PHR1A launch on L1 and Sensor products.
ABSTRACT:Since the beginning of 2012, the first Pleiades-HR satellite of the program conducted by the French National Space Agency, CNES, delivers 20 km wide color scenes with a 70 cm ground sampling distance. A second satellite should be launched in 2013 which will achieve an almost world-wide coverage with a revisit interval of 24h.The assessment of the image quality and the calibration operation have been performed by CNES Image Quality team during the 6 month commissioning phase that followed the satellite launch. The geometric commissioning activities consist in improve the geometric quality of the images in order to meet very demanding specifications as localization accuracy, local coherence, dynamic stability, length alteration … This goal has been achieved through the implementation of new methods of calibration and performance assessment. Some of these methods are based on the exploitation of very specific satellite acquisitions that have been achieved thanks to the amazing agility of the Pleiades satellite. Thus, many stars acquisitions and very slow earth pictures have been processed to characterize dynamic phenomena. Similarly, "along-cross track" pairs have been exploited to improve the accuracy of the focal plane description. This paper deals with these new methods. It describes their accuracy and their operational interests.
The ability to monitor the evolution of the coastal zone over time is an important factor in coastal knowledge, development, planning, risk mitigation, and overall coastal zone management. While traditional bathymetry surveys using echo-sounding techniques are expensive and time consuming, remote sensing tools have recently emerged as reliable and inexpensive data sources that can be used to estimate bathymetry using depth inversion models. Deep learning is a growing field of artificial intelligence that allows for the automatic construction of models from data and has been successfully used for various Earth observation and model inversion applications. In this work, we make use of publicly available Sentinel-2 satellite imagery and multiple bathymetry surveys to train a deep learning-based bathymetry estimation model. We explore for the first time two complementary approaches, based on color information but also wave kinematics, as inputs to the deep learning model. This offers the possibility to derive bathymetry not only in clear waters as previously done with deep learning models but also at common turbid coastal zones. We show competitive results with a state-of-the-art physical inversion method for satellite-derived bathymetry, Satellite to Shores (S2Shores), demonstrating a promising direction for worldwide applicability of deep learning models to inverse bathymetry from satellite imagery and a novel use of deep learning models in Earth observation.
This paper presents a new image restoration pipeline performing especially well on noisy and compressed images. Most images are corrupted by noise. The signal to noise ratio (SNR) level increases with the pixel intensity value, which makes the denoising process especially challenging in dark areas of the images. Moreover, these areas are more likely to be highly compressed since they have low signal variations.In this paper, we take into account compression by introducing a pre-processing step restituting the instrument noise. Then we propose a denoising and deconvolution step optimally parametrized since the instrument response (noise and Modulation Transfer Function) is known. We achieve better restoration than classical algorithms on satellite imagery. This improvement in image quality is shown on two kinds of application: pansharpening and 3D restitution.
Abstract. This paper presents a new Multiview Stereo Pipeline (MVS), called CARS, dedicated to satellite imagery. This pipeline is intended for massive Digital Surface Model (DSM) production and has therefore been designed to maximize scalability robustness and performance. Those two properties have driven the design of the workflow as well as the choice of algorithms and parameter trends, making our pipeline unique with respect to existing solutions in literature. This paper intends to serve as a reference paper for the pipeline implementation, and therefore provides a detailed description of algorithms and workflow. It also demonstrates the pipeline robustness and stability in several use cases, and compares its accuracy with the state-of-the-art pipelines on a reference dataset.
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