Search Results (5)

The multi-satellite on-board observation planning (MSOOP) is a variant of the multi-agent task allocation problem (MATAP). MSOOP is used to complete the observation task allocation in a fully cooperative mode to maximize the profits of the whole system. In this paper, MSOOP for LEO satellite constellations is investigated, and the decentralized algorithm is exploited for solving it. The problem description of MSOOP for LEO satellite constellations is detailed. The coupled constraints make MSOOP more complex than other task allocation problems. The improved Consensus-Based Bundle Algorithm (ICBBA), which includes a bundle construction phase and consensus check phase, is proposed. A constraint check and a mask recovery are introduced into bundle construction and consensus check to handle the coupled constraints. The fitness function is adjusted to adapt to the characteristics of different scenes. Experimental results on series instances demonstrate the effectiveness of the proposed algorithm. Full article

Accurate measurement of the radiative properties of clouds and aerosols is of great significance to global climate change and numerical weather prediction. The multi-angle polarization imager (MAPI) onboard the Fengyun-3 precipitation satellite, planned to be launched in 2023, will provide the multi-angle, multi-shortwave infrared (SWIR) channels and multi-polarization satellite observation of clouds and aerosols. MAPI operates in a non-sun-synchronized inclined orbit and provides images with a spatial resolution of 3 km (sub-satellite) and a swath of 700 km. The observation channels of the MAPI include 1030 nm, 1370 nm, and 1640 nm polarization channels and corresponding non-polarization channels, which provide observation information from 14 angles. In-flight radiometric and polarimetric calibration strategies are introduced, aiming to achieve radiometric accuracy of 5% and polarimetric accuracy of 2%. Simulation experiments show that the MAPI has some unique advantages of characterizing clouds and aerosols. For cloud observation, the polarization phase functions of the 1030 nm and 1640 nm around the scattering angle of a cloudbow show strong sensitivity to cloud droplet radius and effective variance. In addition, the polarized observation of the 1030 nm and 1640 nm has a higher content of information for aerosol than VIS-NIR. Additionally, the unique observation geometry of non-sun-synchronous orbits can provide more radiometric and polarization information with expanded scattering angles. Thus, the multi-angle polarization measurement of the new SWIR channel onboard Fengyun-3 can optimize cloud phase state identification and cloud microphysical parameter inversion, as well as the retrieval of aerosols. The results obtained from the simulations will provide support for the design of the next generation of polarized imagers of China. Full article

Automated extraction of buildings from Earth observation (EO) data is important for various applications, including updating of maps, risk assessment, urban planning, and policy-making. Combining data from different sensors, such as high-resolution multispectral images (HRI) and light detection and ranging (LiDAR) data, has shown great potential in building extraction. Deep learning (DL) is increasingly used in multi-modal data fusion and urban object extraction. However, DL-based multi-modal fusion networks may under-perform due to insufficient learning of “joint features” from multiple sources and oversimplified approaches to fusing multi-modal features. Recently, a hybrid attention-aware fusion network (HAFNet) has been proposed for building extraction from a dataset, including co-located Very-High-Resolution (VHR) optical images and light detection and ranging (LiDAR) joint data. The system reported good performances thanks to the adaptivity of the attention mechanism to the features of the information content of the three streams but suffered from model over-parametrization, which inevitably leads to long training times and heavy computational load. In this paper, the authors propose a restructuring of the scheme, which involved replacing VGG-16-like encoders with the recently proposed EfficientNet, whose advantages counteract exactly the issues found with the HAFNet scheme. The novel configuration was tested on multiple benchmark datasets, reporting great improvements in terms of processing times, and also in terms of accuracy. The new scheme, called HAFNetE (HAFNet with EfficientNet integration), appears indeed capable of achieving good results with less parameters, translating into better computational efficiency. Based on these findings, we can conclude that, given the current advancements in single-thread schemes, the classical multi-thread HAFNet scheme could be effectively transformed by the HAFNetE scheme by replacing VGG-16 with EfficientNet blocks on each single thread. The remarkable reduction achieved in computational requirements moves the system one step closer to on-board implementation in a possible, future “urban mapping” satellite constellation. Full article

Flood detection and produced maps play essential roles in policymaking, planning, and implementing flood management options. Remote sensing is commonly accepted as a maximum cost-effective technology to obtain detailed information over large areas of lands and oceans. We used remote sensing observations from Global Navigation Satellite System-Reflectometry (GNSS-R) to study the potential of this technique for the retrieval of flood maps over the regions affected by the recent flood in the southeastern part of Iran. The evaluation was made using spaceborne GNSS-R measurements over the Sistan and Baluchestan provinces during torrential rain in January 2020. This area has been at a high risk of flood in recent years and needs to be continuously monitored by means of timely observations. The main dataset was acquired from the level-1 data product of the Cyclone Global Navigation Satellite System (CYGNSS) spaceborne mission. The mission consisted of a constellation of eight microsatellites with GNSS-R sensors onboard to receive forward-scattered GNSS signals from the ocean and land. We first focused on data preparation and eliminating the outliers. Afterward, the reflectivity of the surface was calculated using the bistatic radar equations formula. The flooded areas were then detected based on the analysis of the derived reflectivity. Images from Moderate-Resolution Imaging Spectroradiometer (MODIS) were used for evaluation of the results. The analysis estimated the inundated area of approximately 19,644 km² (including Jaz-Murian depression) to be affected by the flood in the south and middle parts of the Sistan and Baluchestan province. Although the main mission of CYGNSS was to measure the ocean wind speed in hurricanes and tropical cyclones, we showed the capability of detecting floods in the study area. The sensitivity of the spaceborne GNSS-R observations, together with the relatively short revisit time, highlight the potential of this technique to be used in flood detection. Future GNSS-R missions capable of collecting the reflected signals from all available multi-GNSS constellations would offer even more detailed information from the flood-affected areas. Full article

The UltraViolet and infrared Sensors at high Quantum efficiency onboard a small SATellite (UVSQ-SAT) mission aims to demonstrate pioneering technologies for broadband measurement of the Earth’s radiation budget (ERB) and solar spectral irradiance (SSI) in the Herzberg continuum (200–242 nm) using high quantum efficiency ultraviolet and infrared sensors. This research and innovation mission has been initiated by the University of Versailles Saint-Quentin-en-Yvelines (UVSQ) with the support of the International Satellite Program in Research and Education (INSPIRE). The motivation of the UVSQ-SAT mission is to experiment miniaturized remote sensing sensors that could be used in the multi-point observation of Essential Climate Variables (ECV) by a small satellite constellation. UVSQ-SAT represents the first step in this ambitious satellite constellation project which is currently under development under the responsibility of the Laboratory Atmospheres, Environments, Space Observations (LATMOS), with the UVSQ-SAT CubeSat launch planned for 2020/2021. The UVSQ-SAT scientific payload consists of twelve miniaturized thermopile-based radiation sensors for monitoring incoming solar radiation and outgoing terrestrial radiation, four photodiodes that benefit from the intrinsic advantages of Ga ${}_2$ O ${}_3$ alloy-based sensors made by pulsed laser deposition for measuring solar UV spectral irradiance, and a new three-axis accelerometer/gyroscope/compass for satellite attitude estimation. We present here the scientific objectives of the UVSQ-SAT mission along the concepts and properties of the CubeSat platform and its payload. We also present the results of a numerical simulation study on the spatial reconstruction of the Earth’s radiation budget, on a geographical grid of 1 $°$ × 1 $°$ degree latitude-longitude, that could be achieved with UVSQ-SAT for different observation periods. Full article