Skybox image and video product evaluation

d’Angelo, Pablo; Máttyus, Gellért; Reinartz, Peter

doi:10.1080/19479832.2015.1109565

Cited by 23 publications

(11 citation statements)

References 9 publications

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“…10c). Smaller voids can be filled, whereby the measured velocities enable the application of a directional interpolator, or the matching window sizes can be automatically adapted to the distribution of ice floes -small windows for dense ice floes and larger ones for sparse ice floes (Debella-Gilo and Kääb, 2011a). The effect of voids on derived parameters can be tested by simulating voids for a rather complete data set (e.g.…”

Section: Discussion Conclusion and Outlookmentioning

confidence: 99%

“…The matching window sizes used in this study for the Plan-etScope data are 30 pixels × 30 pixels (90 m × 90 m) as was found to be roughly optimal from a few tests. However, tests with different window sizes are not the focus of this study (Debella-Gilo and Kääb, 2011a). Measurements with a correlation coefficient smaller than 0.7 are removed and no other post-processing is applied (Kääb et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

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River-ice and water velocities using the Planet optical cubesat constellation

Kääb

Altena

Mascaro

2019

Hydrol. Earth Syst. Sci.

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Abstract. The PlanetScope constellation consists of ∼150 optical cubesats that are evenly distributed like strings of pearls on two orbital planes, scanning the Earth's land surface once per day with an approximate spatial image resolution of 3 m. Subsequent cubesats on each of the orbital planes image the Earth surface with a nominal time lag of approximately 90 s between them, which produces near-simultaneous image pairs over the across-track overlaps of the cubesat swaths. We exploit this short time lag between subsequent Planet cubesat images to track river ice floes on northern rivers as indicators of water surface velocities. The method is demonstrated for a 60 km long reach of the Amur River in Siberia, and a 200 km long reach of the Yukon River in Alaska. The accuracy of the estimated horizontal surface velocities is of the order of ±0.01 m s−1. The application of our approach is complicated by cloud cover and low sun angles at high latitudes during the periods where rivers typically carry ice floes, and by the fact that the near-simultaneous swath overlaps, by design, do not cover the complete Earth surface. Still, the approach enables direct remote sensing of river surface velocities for numerous cold-region rivers at a number of locations and occasionally several times per year – which is much more frequent and over much larger areas than currently feasible. We find that freeze-up conditions seem to offer ice floes that are generally more suitable for tracking, and over longer time periods, compared with typical ice break-up conditions. The coverage of river velocities obtained could be particularly useful in combination with satellite measurements of river area, and river surface height and slope.

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Section: Discussion Conclusion and Outlookmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

River-ice and water velocities using the Planet optical cubesat constellation

Kääb

Altena

Mascaro

2019

Hydrol. Earth Syst. Sci.

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“…Nigerian satellite 2 was a small satellite with a spatial resolution of 2.5 m. The satellite design allowed intentional yaw, which reduced the width of the pixel’s ground track to achieve image SR [ 8 ]. SkySat series satellites used the method of surface array push-frame combined with ground processing, by the sequence image registration and SR algorithm processing on the ground, the image resolution was improved from 1.3 m to 0.9 m, and sub-meter imaging was realized [ 9 , 10 ]. Moreover, a satellite platform was designed to capture images with overlapping parts continuously to solve the problem of poor imaging quality caused by the limited payload of Nanosatellite [ 11 ].…”

Section: Techniques For Improving the Resolution Of Remote-sensingmentioning

confidence: 99%

High-Efficiency Microsatellite-Using Super-Resolution Algorithm Based on the Multi-Modality Super-CMOS Sensor

Zhang

Yang

et al. 2020

Sensors

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To realize the application of super-resolution technology from theory to practice, and to improve microsatellite spatial resolution, we propose a special super-resolution algorithm based on the multi-modality super-CMOS sensor which can adapt to the limited operation capacity of microsatellite computers. First, we designed an oblique sampling mode with the sensor rotated at an angle of 26.56 ∘ ( arctan 1 2 ) to obtain high overlap ratio images with sub-pixel displacement. Secondly, the proposed super-resolution algorithm was applied to reconstruct the final high-resolution image. Because the satellite equipped with this sensor is scheduled to be launched this year, we also designed the simulation mode of conventional sampling and the oblique sampling of the sensor to obtain the comparison and experimental data. Lastly, we evaluated the super-resolution quality of images, the effectiveness, the practicality, and the efficiency of the algorithm. The results of the experiments showed that the satellite-using super-resolution algorithm combined with multi-modality super-CMOS sensor oblique-mode sampling can increase the spatial resolution of an image by about 2 times. The algorithm is simple and highly efficient, and can realize the super-resolution reconstruction of two remote-sensing images within 0.713 s, which has good performance on the microsatellite.

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“…Compared with traditional optical remote sensing images, satellite remote sensing videos have very high spatial and temporal resolution, which can provide users with sufficient dynamic information [1], thus helps users to analyze the motion and instantaneous characteristics of targets more accurately [2]. Therefore, satellite videos can not only play an important role in dynamic monitoring of hot spots [3], dynamic detection of specific targets [4], real-time feedback of traffic conditions [5], dynamic early warning of natural disasters, superresolution of remote sensing images in specific areas [6] [7], but also provide effective support for national defense and economic construction, which makes researches on satellite videos becoming more and more important [8].…”

Section: Introductionmentioning

confidence: 99%

A moving vehicles extraction method in Satellite Videos

Dong

Yang

2020

MATEC Web Conf.

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With the continuous innovation of optical remote sensing technology and the increasing demand for spatial information, satellite videos, which can provide higher spatial and temporal resolution, have been paid a lot of attention. And moving vehicles extraction in satellite videos is one of the most important tasks. By analyzing the shortcomings of current satellite video moving vehicles extraction algorithms, and combining with the characteristics of satellite videos and moving vehicles, this paper proposes an algorithm to extract moving vehicles in satellite videos, that some vehicles are firstly separated from the background by using image extreme points and mean differences, and then the moving vehicles are extracted by joint detection of inter-frame vehicles motion. At the same time, based on the extracted moving vehicles, we also propose a method that can extract road masks by using only three frames. Finally, we use Jilin-1 satellite video data to prove the proposed methods in the experiment. And also this paper has compared the propose methods with another two algorithms, where the results show that the proposed methods greatly improve the accuracy and quality of moving vehicles detection in satellite videos.

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Skybox image and video product evaluation

Cited by 23 publications

References 9 publications

River-ice and water velocities using the Planet optical cubesat constellation

River-ice and water velocities using the Planet optical cubesat constellation

High-Efficiency Microsatellite-Using Super-Resolution Algorithm Based on the Multi-Modality Super-CMOS Sensor

A moving vehicles extraction method in Satellite Videos

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