A review of earth observation resources for secondary school education – part 2

Filchev, Lachezar; Manakos, Ioannis; Reuter, R.; Mardirossian, Garo; Srebrova, Tsveta; Kraleva, Lubomira; Dimitrov, Dimitar; Marini, Kalliroi; Rienow, Andreas

doi:10.3897/arb.v33.e18

Cited by 2 publications

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“…The commercial space industry has more recently sought to help overcome these limitations, especially by demonstrating the capabilities of smaller satellites at much lower costs. Among the technologies with increasing applicability, multi-sensor and hyperspectral imaging on small satellites have become increasingly feasible 2 . While deploying these techniques on small satellite platforms offers potential for advancing remote sensing capabilities, there remain various obstacles such as data accuracy, reliability, the need for atmospheric corrections, processing and storage issues, and platform-specific limitations, which still hinder widespread use 1,2,3 .…”

Section: Introductionmentioning

confidence: 99%

Onboard deep learning for efficient small satellite reflectance retrievals and downlink

Garimella

2023

Image and Signal Processing for Remote Sensing XXIX

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The rapid pace of commercial space launches has drastically increased opportunities for novel small satellite missions. Though small satellites benefit from reduced launch costs, they are constrained by their Size, Weight, and Power (SWaP) limitations. The satellites in MyRadar’s HORIS (Hyperspectral Orbital Remote Imaging Spectrometer) constellation have SWaP constraints of a 1U CubeSat form factor (10 cm3 and ⪅ 1.33 kg), which place significant limits on mission design and duty cycle. In particular, downlinking large amounts of raw data, like that generated by the HORIS narrow band hyperspectral sensor, can be prohibitively bandwidth and power intensive. This study explores using deep learning inference to optimize onboard data processing and to mitigate the impacts of data volume on downlinking hyperspectral information from LEO (Low Earth Orbit). In particular, deep learning inference on the visible wavelength context imagery is used to constrain the aerosol model effects on reflectance retrieval calculations to convert at-sensor radiance measurements into surface (or cloud top) reflectance estimates. Also, a transfer learning approach utilizing an adversarially trained autoencoder to compress data from other satellites is used for HORIS data compression to reduce the required power and bandwidth for alerting use cases.

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Section: Introductionmentioning

confidence: 99%

Onboard deep learning for efficient small satellite reflectance retrievals and downlink

Garimella

2023

Image and Signal Processing for Remote Sensing XXIX

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An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends

Zhao

et al. 2022

Remote Sensing

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As satellite observation technology develops and the number of Earth observation (EO) satellites increases, satellite observations have become essential to developments in the understanding of the Earth and its environment. However, the current impacts to the remote sensing community of different EO satellite data and possible future trends of EO satellite data applications have not been systematically examined. In this paper, we review the impacts of and future trends in the use of EO satellite data based on an analysis of data from 15 EO satellites whose data are widely used. Articles that reference EO satellite missions included in the Web of Science core collection for 2020 were analyzed using scientometric analysis and meta-analysis. We found the following: (1) the number of publications and citations referencing EO satellites is increasing exponentially; however, the number of articles referencing AVHRR, SPOT, and TerraSAR is tending to decrease; (2) papers related to EO satellites are concentrated in a small number of journals: 43.79% of the articles that were reviewed were published in only 13 journals; and (3) remote sensing impact factor (RSIF), a new impact index, was constructed to measure the impacts of EO satellites and to predict future trends in applications of their data. Landsat, Sentinel, MODIS, Gaofen, and WorldView were found to be the most significant current EO satellite missions and MODIS data to have the widest range of applications. Over the next five years (2021–2025), it is expected that Sentinel will become the satellite mission with the greatest influence.

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A review of earth observation resources for secondary school education – part 2

Cited by 2 publications

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Onboard deep learning for efficient small satellite reflectance retrievals and downlink

Onboard deep learning for efficient small satellite reflectance retrievals and downlink

An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends

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