Development of Spaceborne Small Hyperspectral sensor HSC-III for Micro Satellite

2009 ◽  
pp. 331-340 ◽  
Author(s):  
Yoshihide Aoyanagi ◽  
Shin Satori ◽  
Tsuyoshi Totani ◽  
Toshihiko Yasunaka ◽  
Akihiro Nakamura ◽  
...  
Keyword(s):  
Hyperspectral Sensor

The Possibility of Passive Whale Tracking With the Use of An Hyperspectral Sensor

1999 ◽  
Author(s):  
Christina Barnes ◽  
Gary Gilbert ◽  
Jon Schoonmaker ◽  
Jim Rohr
Keyword(s):  
Hyperspectral Sensor

Initial Onboard Calibration Results of the HISUI Hyperspectral Sensor

2021 ◽  
Author(s):  
M. Urai ◽  
S. Tsuchida ◽  
S. Yamamoto ◽  
T. Tachikawa ◽  
A. Iwasaki ◽  
...  
Keyword(s):  
Hyperspectral Sensor

Airborne measurements in the longwave infrared using an imaging hyperspectral sensor

2008 ◽  
Author(s):  
Jean-Pierre Allard ◽  
Martin Chamberland ◽  
Vincent Farley ◽  
Frédérick Marcotte ◽  
Matthias Rolland ◽  
...  
Keyword(s):  
Airborne Measurements ◽  
Hyperspectral Sensor

scholarly journals ICE DETECTION ON AIRPLANE WINGS USING A PHOTOGRAMMETRIC POINT CLOUD: A SIMULATION

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 183-189
Author(s):  
I. Aicardi ◽  
A. Lingua ◽  
L. Mazzara ◽  
M. A. Musci ◽  
G. Rizzo
Keyword(s):  
Point Cloud ◽  
Processing Time ◽  
Field Testing ◽  
Reference Surface ◽  
Steel Panel ◽  
Final Configuration ◽  
Geometric Reconstruction ◽  
Sensor Technique ◽  
Ice Detection ◽  
Hyperspectral Sensor

Abstract. This study describes some tests carried out, within the European project (reference call: MANUNET III 2018, project code: MNET18/ICT-3438) called SEI (Spectral Evidence of ice), for the geometrical ice detection on airplane wings. The purpose of these analysis is to estimate thickness and shape of the ice that an RGB sensor is able to detect on large aircrafts as Boeing 737-800. However, field testing are not available yet, therefore, in order to simulate the final configuration, a steel panel has been used to reproduce the aircraft surface. The adopted methodology consists in defining a reference surface and modelling its 3D shape with and without ice through photogrammetric acquisitions collected by a DJI Mavic Air drone hosting a RGB camera and processed by Agisoft Metashape software. The comparison among models with and without the ice has been presented and results show that it is possible to identify the ice, even though some noise still remains due to the geometric reconstruction itself. Finally, using 3dReshaper and Matlab software, the authors develop various analysis defining the operative limits, the processing time, the correct setting up of Metashape for a more accurate ice detection, the optimization of the methodology in terms of processing time, precision and completeness. The procedure can certainly be more reliable considering the usage of the hyperspectral sensor technique as future implementation.

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