Onboard multispectral data compression using JPEG-like algorithm: a case study

2006 ◽  
Author(s):  
A. Senthil Kumar ◽  
T. Radhika ◽  
P. V. Narashima Rao ◽  
A. S. Manjunath ◽  
K. M. M. Rao
1997 ◽  
Vol 18 (15) ◽  
pp. 3297-3303 ◽  
Author(s):  
D. J. Rogers ◽  
S. I. Hay ◽  
M. J. Packer ◽  
G. R. W. Wint

2001 ◽  
Vol 11 (2) ◽  
pp. 273-288 ◽  
Author(s):  
Sergio Amat ◽  
Francesc Aràndiga ◽  
Albert Cohen ◽  
Rosa Donat ◽  
Gregori Garcia ◽  
...  
Keyword(s):  

2020 ◽  
Vol 12 (2) ◽  
pp. 316
Author(s):  
Vesta Afzali Gorooh ◽  
Subodh Kalia ◽  
Phu Nguyen ◽  
Kuo-lin Hsu ◽  
Soroosh Sorooshian ◽  
...  

Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided a unique dataset to characterize cloud types. However, data from this nadir-looking radar offers limited capability for estimating precipitation because of the narrow satellite swath coverage and low temporal frequency. We use these high-quality observations to build a Deep Neural Network Cloud-Type Classification (DeepCTC) model to estimate cloud types from multispectral data from the Advanced Baseline Imager (ABI) onboard the GOES-16 platform. The DeepCTC model is trained and tested using coincident data from both CloudSat and ABI over the CONUS region. Evaluations of DeepCTC indicate that the model performs well for a variety of cloud types including Altostratus, Altocumulus, Cumulus, Nimbostratus, Deep Convective and High clouds. However, capturing low-level clouds remains a challenge for the model. Results from simulated GOES-16 ABI imageries of the Hurricane Harvey event show a large-scale perspective of the rapid and consistent cloud-type monitoring is possible using the DeepCTC model. Additionally, assessments using half-hourly Multi-Radar/Multi-Sensor (MRMS) precipitation rate data (for Hurricane Harvey as a case study) show the ability of DeepCTC in identifying rainy clouds, including Deep Convective and Nimbostratus and their precipitation potential. We also use DeepCTC to evaluate the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) product over different cloud types with respect to MRMS referenced at a half-hourly time scale for July 2018. Our analysis suggests that DeepCTC provides supplementary insights into the variability of cloud types to diagnose the weakness and strength of near real-time GEO-based precipitation retrievals. With additional training and testing, we believe DeepCTC has the potential to augment the widely used PERSIANN-CCS algorithm for estimating precipitation.


2014 ◽  
Vol 6 (11) ◽  
pp. 10860-10887 ◽  
Author(s):  
Stephen Grebby ◽  
Dickson Cunningham ◽  
Kevin Tansey ◽  
Jonathan Naden

2020 ◽  
Vol 12 (24) ◽  
pp. 4180
Author(s):  
Athanasia-Maria Tompolidi ◽  
Olga Sykioti ◽  
Konstantinos Koutroumbas ◽  
Issaak Parcharidis

The aim of this study was to propose a methodology that provides a detailed description of the argillic zone of a hydrothermal field, based on satellite multispectral data. More specifically, we developed a method based on spectral unmixing where hydroxyl-bearing alteration is represented by a single endmember (representing clays) and the three (nearly) non-altered primary volcanic lithologies, namely, two types of lava flows (basic and acidic compositions) and the loose materials (alluvial/beach deposits, scree, pyroclastic deposits, etc.), are represented by three endmembers. We also used one endmember representing elemental sulfur that is present in fumarolic vents hosted by active hydrothermal craters. The methodology was applied in the south part of Lakki plain inside the Nisyros volcano caldera (Greece), using Sentinel-2, Landsat-8/OLI, and ASTER satellite multispectral datasets. Specifically, it was applied separately to each one of the three datasets. The spectral unmixing results, combined with the relative geological map, provide quantitative estimations of the primary volcanic and loose material areas affected by alteration. In addition, pixels with high abundance values of hydroxyl-bearing alteration corresponded to mapped areas with strong hydrothermal alteration. The developed methodology is superior to conventional approaches (e.g., alteration spectral index) in terms of its ability to describe the overall pattern of the hydrothermal field. The most accurate results were taken when applied to ASTER or Sentinel-2 MSI data.


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