OGC-compatible high-performance web map service for remote sensing data visualization

2010 ◽  
Author(s):  
Chunyang Hu ◽  
Yongwang Zhao ◽  
Jing Li ◽  
Min Liu ◽  
Dianfu Ma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Visualization ◽  
High Performance ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Web Map ◽  
Map Service

High performance GPU computing based approaches for oil spill detection from multi-temporal remote sensing data

2017 ◽  
Vol 202 ◽  
pp. 28-44 ◽  
Author(s):  
Ujwala Bhangale ◽  
Surya S. Durbha ◽  
Roger L. King ◽  
Nicolas H. Younan ◽  
Rangaraju Vatsavai
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
High Performance ◽  
Gpu Computing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Oil Spill Detection ◽  
Multi Temporal

scholarly journals Conceptual low-cost on-board high performance computing in CubeSat nanosatellites for pattern recognition in Earth's remote sensing

10.29007/8d25 ◽  
2019 ◽  
Author(s):  
J J Hernández-Gómez ◽  
G A Yañez-Casas ◽  
Alejandro M Torres-Lara ◽  
C Couder-Castañeda ◽  
M G Orozco-del-Castillo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Performance Computing ◽  
High Performance ◽  
Low Cost ◽  
Remote Sensing Data ◽  
Artificial Satellites ◽  
Severe Problem ◽  
Sensing Data ◽  
Low Earth Orbits ◽  
Performance Computing

Nowadays, remote sensing data taken from artificial satellites require high space com- munications bandwidths as well as high computational processing burdens due to the vertiginous development and specialisation of on-board payloads specifically designed for remote sensing purposes. Nevertheless, these factors become a severe problem when con- sidering nanosatellites, particularly those based in the CubeSat standard, due to the strong limitations that it imposes in volume, power and mass. Thus, the applications of remote sensing in this class of satellites, widely sought due to their affordable cost and easiness of construction and deployment, are very restricted due to their very limited on-board computer power, notwithstanding their Low Earth Orbits (LEO) which make them ideal for Earth’s remote sensing. In this work we present the feasibility of the integration of an NVIDIA GPU of low mass and power as the on-board computer for 1-3U CubeSats. From the remote sensing point of view, we present nine processing-intensive algorithms very commonly used for the processing of remote sensing data which can be executed on-board on this platform. In this sense, we present the performance of these algorithms on the proposed on-board computer with respect with a typical on-board computer for CubeSats (ARM Cortex-A57 MP Core Processor), showing that they have acceleration factors of average of 14.04× ∼14.72× in average. This study sets the precedent to perform satellite on-board high performance computing so to widen the remote sensing capabilities of CubeSats.

High-performance mshing processing of remote sensing data on large displays

2014 ◽  
Vol 8 (1) ◽  
pp. 084796
Author(s):  
Tung-Ju Hsieh ◽  
Wei-Yao Chen ◽  
Che-Hao Chang ◽  
Yen-Lin Chen ◽  
Ming-Li Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Performance ◽  
Remote Sensing Data ◽  
Large Displays ◽  
Sensing Data

scholarly journals DYNAMICAL PREDICTION TECHNIQUE FOR GEOSIMULATION USING MULTISPECTRAL REMOTE SENSING DATA

2018 ◽  
Vol IV-4 ◽  
pp. 213-219
Author(s):  
I. E. Villalon-Turrubiates
Keyword(s):  
Remote Sensing ◽  
High Performance ◽  
Remote Sensing Data ◽  
Behavioral Model ◽  
Geographic Region ◽  
Evolutionary Information ◽  
Computational Techniques ◽  
Sensing Data ◽  
Remote Sensing Systems ◽  
Multispectral Remote Sensing

<p><strong>Abstract.</strong> The analysis of dynamical models for prediction and geosimulation using the information extracted from a geographical region processed from the data provided by multispectral remote sensing systems provides useful information for urban planning and resource management. However, several topics of interest on this particular matter are still to be properly studied. Using the remote sensing data that has been extracted from multispectral images from a particular geographic region in discrete time, its dynamic study is performed in both, spatial resolution and time evolution, in order to obtain the dynamical model of the physical variables and the evolutionary information about the data. This provides a background for understanding the future trends in development of the dynamics inherent in the multispectral and high-resolution images. This proposition is performed via an intelligent computational paradigm based on the use of dynamical filtering techniques modified to enhance the quality of reconstruction of the data extracted from multispectral remote sensing images and using high-performance computational techniques to unify the available data scheme with its dynamic analysis and, therefore, provide a behavioral model of the sensed data.</p>

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