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The generation of photogrammetric point clouds from satellite images is often based on image correlation techniques. Correlation errors can arise for a wide variety of reasons: transient objects, homogeneous areas, shadows, and surface discontinuities. Therefore, a simple 3D Gaussian distribution at the point cloud level is not an appropriate model. In this paper, we propose a new point cloud denoising method integrated into the Multiview Stereo Pipeline CARS, dedicated to satellite imagery. Building upon bilateral filtering principles, our approach introduces a novel utilization of color information, confidence estimation and geometric constraints alongside point positions and normals. While the use of point color increases the level of detail, the addition of geometric constraints and confidence awareness guides processing towards a realistic solution. We propose an ablation study and compare our solution against a previously established bilateral filter with LiDAR data as ground truth.

In a 3D reconstruction pipeline, stereo matching step aims at computing a disparity map representing the depth between image pair. The evaluation of the disparity map can be done through the estimation of a confidence metric. In this article, we propose a new confidence metric, named ambiguity integral metric, to assess the quality of the produced disparity map. This metric is derived from the concept of ambiguity, which characterizes the property of the cost curve profile. It aims to quantify the difficulty in identifying the correct disparity to select. The quality of ambiguity integral metric is evaluated through the ROC curve methodology and compared with other confidence measures. In regards to other measures, the ambiguity integral measure shows a good potential. We also integrate this measure through various steps of the stereo matching pipeline in order to improve the performance estimation of the disparity map. First, we include ambiguity integral measure during the Semi Global Matching optimization step. The objective is to weight, by ambiguity integral measure, the influence of points in the SGM regularization to reduce the impact of ambiguous points. Secondly, we use ambiguity as an input of a disparity refinement deep learning architecture in order to easily locate noisy area and preserve details.

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.

Earth Observation (EO) remote sensing missions are producing an increasing volume of data due to higher spatial and spectral resolutions, and higher frequency of acquisitions. Thus, in order to prepare the future of image processing pipelines, CNES has carried out Research & Development studies related to the use of Big Data and Cloud technologies for image processing chains made. Since mid-2019, CNES in partnership with Airbus Defense & Space, has started a new High Resolution Optical EO mission dedicated to very high resolution 3D observation called CO3D (“Constellation Optique 3D”). To achieve those objectives, a new image processing pipeline prototype is being developed taking in consideration the lessons learned from the previous studies. The paper will introduce this new image processing pipeline, the processing paradigms used to take advantage of big data technologies and the results of production benchmarks at a large scale. The on-going works to optimize the processing pipeline and Cloud cluster will be also discussed.

Several 3D reconstruction pipelines are being developed around the world for satellite imagery. Most of them implement their own versions of Semi-Global Matching, as an option for the matching step. However, deep learning based solutions already outperform every SGM derived algorithms on Kitti and Middlebury stereo datasets. But these deep learning based solutions need huge quantities of ground truths for training. This implies that the generation of ground truth stereo datasets, from satellite imagery and lidar, seems to be of great interest for the scientific community. It will aim at reducing the potential transfer learning difficulties, that could arise from a training done on datasets such as Middlebury or Kitti. In this work, we present a new ground truth generation pipeline. It produces stereo-rectified images and ground truth disparity maps, from satellite imagery and lidar. We also assess the rectification and the disparity accuracies of these outputs. We finally train a deep learning network on our preliminary ground truth dataset.

In the frame of the Copernicus program of the European Commission, Sentinel-2 is a constellation of 2 satellites with a revisit time of 5 days in order to have temporal images stacks and a global coverage over terrestrial surfaces. Satellite 2A was launched in June 2015, and satellite 2B will be launched in March 2017. In cooperation with the European Space Agency (ESA), the French space agency (CNES) is in charge of the image quality of the project, and so ensures the CAL/VAL commissioning phase during the months following the launch. This cooperation is also extended to routine phase as CNES supports European Space Research Institute (ESRIN) and the Sentinel-2 Mission performance Centre (MPC) for validation in geometric and radiometric image quality aspects, and in Sentinel-2 GRI geolocation performance assessment whose results will be presented in this paper. The GRI is a set of S2A images at 10m resolution covering the whole world with a good and consistent geolocation. This ground reference enables accurate multi-temporal registration of refined Sentinel-2 products. While not primarily intended for the generation of DSM, Sentinel-2 swaths overlap between orbits would also allow for the generation of a complete DSM of land and ices over 60° of northern latitudes (expected accuracy: few S2 pixels in altimetry). This DSM would benefit from the very frequent revisit times of Sentinel-2, to monitor ice or snow level in area of frequent changes, or to increase measurement accuracy in areas of little changes.

Context: Urine color (Uc) is used to asses urine concentration when laboratory techniques are not feasible., Objective: To compare the accuracy of Uc scoring using 4 light conditions and 2 scoring techniques with a 7-color Uc chart. Additionally, to assess the results' generalizability, a subsample was compared with scores obtained from fresh samples., Design: Descriptive laboratory study., Samples: A total of 178 previously frozen urine samples were scored, and 78 samples were compared with their own fresh outcomes., Main Outcome Measure(s): Urine color and accuracy for classifying urine samples were calculated using receiver operating characteristics analysis, allowing us to compare the diagnostic capacity against a 1.020 urine specific gravity cutoff and defining optimal Uc cutoff value., Results: Urine color was different among light conditions (P < .01), with the highest accuracy (80.3%) of correct classifications of low or high urine concentrations occurring at the brightest light condition. Lower light intensity scored 1.5 to 2 shades darker on the 7-color Uc scale than bright conditions (P < .001), but no further practical differences in accuracy occurred between scoring techniques. Frozen was 0.5 to 1 shade darker than freshly measured Uc (P < .004), but the values were moderately correlated (r = 0.64). A Bland-Altman plot showed that reporting bias mainly affected darker Uc without affecting the diagnostic ability of the method., Conclusions: Urine color scoring, accuracy, and Uc cutoff values were affected by lighting condition but not by scoring technique, with greater accuracy and a 1-shade-lower Uc cutoff value at the brightest light (ie, light-emitting diode flashlight)., (© by the National Athletic Trainers' Association, Inc.)