James is the President/CEO of HySpeed Computing, a technology start-up in Miami, FL, whose mission is to provide the most effective analysis tools for deriving and delivering information from geospatial imagery. James obtained a BA in Engineering from Dartmouth College, a MS in Civil Engineering from University of Colorado, Boulder, and a PhD in Hydrologic Sciences from University of California, Davis. His expertise includes remote sensing, software development, mathematical modeling, imaging spectrometry, and high-performance computing. You can follow James on his company blog at hyspeedblog.wordpress.com.
Author: James Goodman
Below we explore the role of cloud computing in geospatial image processing, and the advantages this technology provides to the overall remote sensing toolbox.
The underlying concept of cloud computing is not new; dating back to the advent of the client-server model in mainframe computing, where the utilization of local devices to perform tasks on a server, or set of connected servers, has a long history within the computing industry.
With the rise of the personal computer, and the relative cost efficiency of memory and processing speed for these systems, there ensued a similarly rich history of computing using the local desktop environment.
As a result, in many application domains, including that of remote sensing, a dichotomy developed in the computing industry, with a large portion of the user community reliant on personal computers and mostly the government and big business utilizing large-scale servers.
More recently, however, there has been an industry-wide surge in the prevalence of cloud computing applications within the general user community. Driven in large part by rapidly growing data volumes and the profound increase and diversity of mobile computing devices, as well as a desire for access to centralized analytics, cloud computing is now a common component in our everyday experience.
Where does cloud computing fit within remote sensing? Given the online availability of weather maps and high-resolution satellite base maps, it can be argued that cloud computing is already regularly used in remote sensing. However, there are an innumerable number of other remote sensing applications, with societal and economic benefits, that are not currently available in the cloud.
Since most of these applications are not directed at the consumer market, but instead relevant predominantly to business, government, education and scientific concerns, what then are the advantages of cloud computing in remote sensing?
At HySpeed Computing we are partnering with Exelis Visual Information Solutions to develop a cloud computing platform for processing data from the Hyperspectral Imager for the Coastal Ocean (HICO) – a uniquely capable sensor located on the International Space Station (ISS). The backbone of the computing framework is based on the ENVI Services Engine, with a user interface built using open-source software tools such as GeoServer and Leaflet.
A prototype version of the web-enabled HICO processing system will soon be publically available for testing and evaluation by the community. Links to access the system will be provided on our website once it is released.
We envision a remote sensing future where the line between local and cloud computing becomes obscured, where applications can be interchangeably run in any computing environment, where developers can utilize their programming language of choice, where scientific achievements and innovations are readily shared through a distributed processing network, and where image-derived information is rapidly distributed to the global user community.
And what’s most significant about this vision is that the future is closer than you imagine.
About HySpeed Computing: Our mission is to provide the most effective analysis tools for deriving and delivering information from geospatial imagery. Visit us at hyspeedcomputing.com.
Categories: ENVI Blog | Imagery Speaks
Tags: ENVI, Cloud, Remote Sensing, Image Processing, geospatial imagery, geospatial, HICO
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