scholarly journals Infrared detector activities at NASA Langley Research Center

2008 ◽  
Vol 1076 ◽  
Author(s):  
M. Nurul Abedin ◽  
Tamer F Refaat ◽  
Oleg V Sulima ◽  
Farzin Amzajerdian

ABSTRACTInfrared detector development and characterization at NASA Langley Research Center will be reviewed. These detectors were intended for ground, airborne, and space borne remote sensing applications. Discussion will be focused on recently developed single-element infrared detector and future development of near-infrared focal plane arrays (FPA). The FPA will be applied to next generation space-based instruments. These activities are based on phototransistor and avalanche photodiode technologies, which offer high internal gain and relatively low noise-equivalent-power. These novel devices will improve the sensitivity of active remote sensing instruments while eliminating the need for a high power laser transmitter.

2020 ◽  
pp. 35
Author(s):  
M. Campos-Taberner ◽  
F.J. García-Haro ◽  
B. Martínez ◽  
M.A. Gilabert

<p class="p1">The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.</p>


2020 ◽  
Vol 12 (21) ◽  
pp. 3469
Author(s):  
Bilawal Abbasi ◽  
Zhihao Qin ◽  
Wenhui Du ◽  
Jinlong Fan ◽  
Chunliang Zhao ◽  
...  

The atmosphere has substantial effects on optical remote sensing imagery of the Earth’s surface from space. These effects come through the functioning of atmospheric particles on the radiometric transfer from the Earth’s surface through the atmosphere to the sensor in space. Precipitable water vapor (PWV), CO2, ozone, and aerosol in the atmosphere are very important among the particles through their functioning. This study presented an algorithm to retrieve total PWV from the Chinese second-generation polar-orbiting meteorological satellite FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) data, which have three near-infrared (NIR) water vapor absorbing channels, i.e., channel 16, 17, and 18. The algorithm was improved from the radiance ratio technique initially developed for Moderate-Resolution Imaging Spectroradiometer (MODIS) data. MODTRAN 5 was used to simulate the process of radiant transfer from the ground surfaces to the sensor at various atmospheric conditions for estimation of the coefficients of ratio technique, which was achieved through statistical regression analysis between the simulated radiance and transmittance values for FY-3D MERSI-2 NIR channels. The algorithm was then constructed as a linear combination of the three-water vapor absorbing channels of FY-3D MERSI-2. Measurements from two ground-based reference datasets were used to validate the algorithm: the sun photometer measurements of Aerosol Robotic Network (AERONET) and the microwave radiometer measurements of Energy’s Atmospheric Radiation Measurement Program (ARMP). The validation results showed that the algorithm performs very well when compared with the ground-based reference datasets. The estimated PWV values come with root mean square error (RMSE) of 0.28 g/cm2 for the ARMP and 0.26 g/cm2 for the AERONET datasets, with bias of 0.072 g/cm2 and 0.096 g/cm2 for the two reference datasets, respectively. The accuracy of the proposed algorithm revealed a better consistency with ground-based reference datasets. Thus, the proposed algorithm could be used as an alternative to retrieve PWV from FY-3D MERSI-2 data for various remote sensing applications such as agricultural monitoring, climate change, hydrologic cycle, and so on at various regional and global scales.


Author(s):  
Sarath D. Gunapala ◽  
Sir (Don) B. Rafol ◽  
David Z. Ting ◽  
Alexander Soibel ◽  
Arezou Khoshakhlagh ◽  
...  

Author(s):  
Sarath D. Gunapala ◽  
Sir B. Rafol ◽  
David Z. Ting ◽  
Alexander Soibel ◽  
Arezou Khoshakhlagh ◽  
...  

2005 ◽  
Vol 11 (11) ◽  
pp. 1339-1356 ◽  
Author(s):  
Adam L. Webster ◽  
William H. Semke

The ability to eliminate, or effectively control, vibration in remote sensing applications is critical. Any perturbations of an imaging system are greatly magnified over the hundreds of kilometers from the orbiting space platform to the Earth's surface. Space platforms, such as the International Space Station, are not as predictable or stable as many other spacecraft. Therefore, an effective vibration isolation and/or absorber system is needed that operates over a wide range of excitation frequencies. A passive system is also preferred to reduce the resources required, as well as to provide a reliable and self-contained system. To accomplish these goals, a vibration amplitude limiting system has been developed that uses both vibration isolation and absorber components. Viscoelastic structural elements that act as both a spring and a damper in a single element are implemented in the design. This configuration also demonstrates a favorable frequencydependent response and produces a system with improved dynamic behavior compared to conventional spring and damper designs. This rotation limiting vibration system has been designed and analyzed for use in digital remote sensing imaging. The transmissibility and the ground jitter associated with the system are determined. A summary of these results will be presented along with a comparison to a more conventional vibration isolation/absorber system.


1988 ◽  
Author(s):  
Mary Bothwell ◽  
Gary C. Bailey ◽  
Valerie G. Wright ◽  
Kadri Vural ◽  
Michael A. Blessinger

2021 ◽  
Vol 8 ◽  
Author(s):  
Tenghui Ouyang ◽  
Ximiao Wang ◽  
Shaojing Liu ◽  
Huanjun Chen ◽  
Shaozhi Deng

Two-dimensional (2D)-material-based photodetectors have recently received great attention due to their potentials in developing ultrathin and highly compact devices. Avalanche photodiodes (APDs) are widely used in a variety of fields such as optical communications and bioimaging due to their fast responses and high sensitivities. However, conventional APDs based on bulk materials are limited by their relatively high dark current. One solution to tackle this issue is by employing nanomaterials and nanostructures as the active layers for APDs. In this study, we proposed and fabricated an atomically-thick APD based on heterojunctions formed by 2D transition metal dichalcogenides (TMDs). A typical device structure was formed by stacking a semiconducting monolayer WS2 onto two metallic few-layer MoTe2 flakes. Due to the Schottky barrier formed between the TMD layers and their atomic thicknesses, the dark current of the APD is greatly reduced down to 93 pA. In addition, the APD can operate through a broad spectral range from visible to near-infrared region, with a responsivity of 6.02 A/W, an external quantum efficiency of 1,406%, and an avalanche gain of 587. We believe that the 2D APD demonstrated here provides a feasible approach for developing all-2D optoelectronic devices with simultaneous high-sensitivity and low noise.


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