Infrared detectors for space applications

Fick, Wolfgang; Gassmann, Kai Uwe; Haas, L. D.; Haiml, M.; Hanna, S.; Hübner, D.; Höhnemann, Holger; Nothaft, H.-P.; Thöt, R.

doi:10.1515/aot-2013-0046

Cited by 13 publications

(4 citation statements)

References 6 publications

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“…For a given PV-type detector, its normalized detectivity D * and its noise current i noise can be given by [39]:…”

Section: Conclusion Discussion and Future Workmentioning

confidence: 99%

Towards Artificial Intelligence Based Radiometric Calibration for In-orbit Remote Sensing Satellites

Chen,

Wu,

Hui

et al. 2024

Preprint

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Radiometric Calibration (RC) is a key step in high-precision aerospace infrared remote sensing. Currently, source-based RC is the most prevailing methodologies, where a radiometric source such as blackbody with known radiation energy is observed by the sensor and the corresponding output digital number is recorded. However, the methodology is limited by several constraints, e.g., space and weight requirement for in-orbit blackbodies, and lack of uniformity and ideal properties of blackbodies. This paper presents a new artificial intelligence based RC that directly generates RC coefficients given the state of the sensor parameters. First, we derive the relationship between the RC coefficients and the physical parameters and environmental parameters of the sensor. Next, we steadily collect a large quantity of remote sensing data using our in-orbit satellites since 2018 and correspondingly train a neural network to fit such function for the generation of RC coefficients. Finally, our extensive experiments show that the proposed method achieves high-accuracy RC comparable with state-of-the-art methods that use in-orbit blackbody. To the best of our knowledge, we are the first to demonstrate an artificial-intelligence-based RC method for satellites without observation to blackbodies during in-orbit remote sensing. We anticipate that the new approach could promote the accuracy and uniformity of global infrared remote sensing data, thereby promoting the ability to discern natural laws and understand longterm environmental or celestial trends. Besides, it can substantially save the cost for carrying and observing in-orbit blackbodies.

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“…For a given PV-type detector, its normalized detectivity D * and its noise current i noise can be given by [39]:…”

Section: Conclusion Discussion and Future Workmentioning

confidence: 99%

Towards Artificial Intelligence Based Radiometric Calibration for In-orbit Remote Sensing Satellites

Chen,

Wu,

Hui

et al. 2024

Preprint

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show abstract

“…AIM is one of a view world-wide leading suppliers of high-performance infrared detectors and focal plane subassemblies [12]. This paper reports on our current status of low-dark-current p/n and n/p two-dimensional LWIR/VLWIR planar MCT technology.…”

Section: Conlusionsmentioning

confidence: 99%

Below Rule’07 low dark current LWIR and VLWIR MCT 2D focal plane detector arrays from AIM

Haiml

Eich

Fick

et al. 2019

International Conference on Space Optics — ICSO 2016

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“…2 Thermal emission from hot objects in the MWIR region can be tracked and thus has a pivotal role in military and civilian night vision applications. 3 There is also a push to expand the operational spectrum of Si photonics from the near-infrared (NIR, 0.75-1.4 mm) to MWIR to increase the bandwidth. [4][5][6][7] The widely used materials for MWIR photodetection are HgCdTe, InSb, and InAs/GaSb type-II super lattices (T2SLs).…”

Section: Introductionmentioning

confidence: 99%