National satellite image coverage using overall planning technique

Zhang, Tao; Lei, Bing; You-min, Gan; Hu, Yizhi; Liu, Ke

doi:10.1109/igarss.2016.7730430

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…In aerial panoramas, image acquisition can be performed by satellites or UAV systems, but satellite technology provides a higher coverage area than that of other systems such as UAVs [23]. One advantage is that satellite image acquisition is faster than that of UAVs [24]. However, there are important factors to evaluate for the use of satellite image acquisition: firstly, if an analysis of a specific section is required, it is necessary to check if any satellite is available for the specific coordinates or has recent information on the area of interest; additionally, if the area is small, when zooming to focus on it, the resolution will be lower in comparison to that of a medium-size UAV camera; an additional consideration is UAVs' dependence on the state of the clouds, owing to the fact that satellites are not subjected to weather inclemency, such as storms, as UAVs are; for satellite aerial panorama, the methods for mosaicing images are based on cross-correlation, Fourier-based, phase correlation, and area-based approaches [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Image Mosaicing Applied on UAVs Survey

et al. 2022

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The use of UAV (unmanned aerial vehicle) technology has allowed for advances in the area of robotics in control processes and application development. Such is the case of image processing, in which, by the use of aerial photographs taken by these aircrafts, it is possible to perform surveillance and monitoring tasks. As an example, we can mention the use of aerial photographs for the generation of panoramic images through the process of stitching images without losing image resolution. Some applications are photogrammetry and mapping, where the main problems to be solved are image alignment and ghosting images, for which different stitching techniques can be applied. These methodologies can be categorized into direct methods or feature-based methods. This paper aims to show an overview of the most frequently applied mosaicing techniques in UAVs by providing an introduction to those interested in developing in this area. For this purpose, a summary of the most applied techniques and their applications is given, showing the trend of the research field and the contribution of different countries over time.

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Section: Introductionmentioning

confidence: 99%

Image Mosaicing Applied on UAVs Survey

et al. 2022

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“…For instance, Long et al [17] designed an improved multi-satellite cooperative task planning algorithm (MSCTP-CEM) based on a co-evolution mechanism. Zhang et al [18] introduced an overall planning technique to solve the problem of earth satellite schedule. In contrast, Kilic and Ozkan [19] proposed an ant colony optimization metaheuristic approach to solve the broadcast's scheduling problem.…”

Section: Research On Ground Stations Scheduling Problemmentioning

confidence: 99%

Reinforcement learning-based ground stations scheduling algorithm for LEO satellites

Chen

Xiao

Jicheng

et al. 2022

Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022)

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With the rapidly increasing number and movement of satellites, the ground stations scheduling problem for Low Earth Orbit (LEO) satellites becomes more complex due to its over-constraint nature. In this paper, we propose a novel ground stations scheduling algorithm named Reinforcement Learning-Based Ground Stations Scheduling Algorithm (RLBGSSA). Its batch tasks planning branch (RLBGSBSA) ensures ground stations efficiently track satellites and achieve timeliness. Under the state constraints, the individual task planning branch of RLBGSSA (RLBGSISA) generates the feasible planning combinations based on reinforcement learning, and gains the combination constraints to filter the planning combinations. To maintain the stability of tasks and equipment, the optimal combination among the screened results is selected via a designed value function, which can achieve minimal changes to planned tasks. Furthermore, RLBGSBSA can reset the scheduling of ground stations to void planning overflow, when the number of individual tasks newly added is too large. Finally, the rationality and effectiveness of our algorithm are verified by numerical simulation.

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“…With involvement of local surveying and mapping departments across the country, the land cover mapping was achieved by the basic workflow of 1) base map production, 2) remote sensing data collection and processing, 3) field survey and verification, and 4) GCC data set construction. Finally, and importantly, since the geographic conditions might experience change during the census period, the GCC data set was checked and approved to the standard time point June 30, 2015, which means that the census data reflect the land cover status around June 30, 2015 [30].…”

Section: Geographic Conditions Census Datamentioning

confidence: 99%

UNVI-Based Time Series for Vegetation Discrimination Using Separability Analysis and Random Forest Classification

Liu

Zhang

et al. 2020

Remote Sensing

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Land cover data is crucial for earth system modelling, natural resources management, and conservation planning. Remotely sensed time-series data capture dynamic behavior of vegetation, and have been widely used for land cover mapping. Temporal profiles of vegetation index (VI), especially normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), are the most used features derived from time-series spectral data. Whether NDVI or EVI is optimal to generate temporal profiles has not been evaluated. The universal normalized vegetation index (UNVI), a relatively new index with all spectral bands incorporated, has been proved to be more effective than several commonly used satellite-derived VIs in some application scenarios. In this study, we explored the ability of UNVI time series for discriminating different vegetation types in Chaoyang prefecture, northeast China, in comparison with normalized NDVI, EVI, triangle vegetation index (TVI), and tasseled cap transformation greenness (TCG). These five indices were calculated using Landsat 8 surface reflectance data, and two comparative experiments were conducted. The first experiment analyzed class separabilities using pairwise JM (Jeffries–Matusita) distance as indicator, and the results showed that UNVI was superior to EVI, TVI, and TCG, and almost equivalent to NDVI, especially during the peak of vegetation growing season and for the most indistinguishable vegetation pair broadleaf and shrubs. The second experiment compared the vegetation classification accuracies using the features of these VI temporal profiles and the corresponding phenological parameters, and the results showed that UNVI can better classify the five major vegetation in Chaoyang prefecture than other four indices. Therefore, we conclude that UNVI time series has considerable potential for regional land cover mapping, and we recommend that the use of the UNVI is considered in the future time series related studies.

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National satellite image coverage using overall planning technique

Cited by 4 publications

References 2 publications

Image Mosaicing Applied on UAVs Survey

Image Mosaicing Applied on UAVs Survey

Reinforcement learning-based ground stations scheduling algorithm for LEO satellites

UNVI-Based Time Series for Vegetation Discrimination Using Separability Analysis and Random Forest Classification

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