A Year of Hyperspectral Image Processing in the Cloud – HICO IPS reaches a milestone

It has been a little more than one year since we first launched the HICO Image Processing System (HICO IPS), and its performance continues to be exceptional. In fact, as a prototype, HICO IPS has exceeded all expectations, working flawlessly since it was first launched in May 2015.

HICO IPS

HICO IPS is a web-application for on-demand remote sensing image analysis that allows users to interactively select images and algorithms, dynamically launch analysis routines, and then see results displayed directly in an online map interface. More details are as follows:

  • System developed to demonstrate capabilities for remote sensing image analysis in the cloud
  • Software stack utilizes a combination of commercial and open-source software
  • Core image processing and data management performed using ENVI Services Engine
  • Operational system hosted on Rackspace cloud server
  • Utilizes imagery from the Hyperspectral Imager for the Coastal Ocean (HICO), which was deployed on the International Space Station (ISS) from 2009-2014
  • Example algorithms are included for assessing coastal water quality and other nearshore environmental conditions
  • Application developed in collaboration between HySpeed Computing and Exelis Visual Information Solutions (now Harris Geospatial Solutions)
  • Project supported by the Center for the Advancement of Science in Space (CASIS)

And here’s a short overview of HICO IPS accomplishments and performance in the past year, including some infographics to help illustrate how the system has been utilized:

  • The application has received over 5000 visitors
  • Users represent over 100 different countries
  • System has processed a total of 1000 images
  • Equivalent area processed is 4.5 million square kilometers
  • The most popular scene selected for analysis was the Yellow River
  • The most popular algorithm was Chlorophyll followed closely by Land Mask
  • Application has run continuously without interruption since launch in May 2015

HICO IPS infographics

Try it out today for yourself: http://hyspeedgeo.com/HICO/

Remote Sensing in the Cloud – Characterizing shallow coastal environments using HICO IPS

This is the last in a series of posts devoted to reviewing the algorithms currently implemented in the cloud-based HICO Image Processing System (HICO IPS). Links to the other posts in this series are provided below. Here we provide an overview of the algorithm utilized for retrieving multiple layers of water and habitat information for shallow coastal environments.

HICO IPS

Objective – Demonstrate the capability to simultaneously derive information on water properties, water depth and seabed features in shallow coastal environments using a complex hyperspectral image processing algorithm.

Algorithm – Derives inherent optical properties for absorption and backscattering (phytoplankton absorption, detritus and gelbstoff absorption, particle backscattering, total absorption, and total backscattering [m-1]), bottom albedo (reflectance at 550 nm), and water depth (depth [m]) using a semi-analytical inversion model (Goodman and Ustin 2007; Lee et al. 1999, 1998).

Note that this algorithm uses a computationally intensive optimization scheme, which can result in long processing times for large areas. As a reference, the example shown here required approximately 15 minutes to generate output. It is suggested that users select relatively small regions-of-interest when implementing this algorithm.

Inputs – User specified HICO scene, with optional region-of-interest; optional NDWI land/water mask, with user adjustable NDWI threshold; and specification of desired output parameter.

HICO IPS Shark Bay subset

Output – Selected output parameter depicted using a blue-red color ramp, where blue represents low values and red represents high values. If the NDWI land/water mask was selected, then the selected optical property is only calculated and mapped for the water pixels.

HICO IPS Shark Bay aquacor

Try it out today for yourself: http://hyspeedgeo.com/HICO/

 

Related posts

Introducing the HICO Image Processing System

Calculating a land/water mask using HICO IPS

Deriving chlorophyll concentration using HICO IPS

Evaluating water optical properties using HICO IPS

 

References

Goodman J, Ustin SL (2007) Classification of benthic composition in a coral reef environment using spectral unmixing, Journal of Applied Remote Sensing, vol. 1(1), 011501-011501.

Lee Z, Carder KL, Mobley CD, Steward RG, Patch JS (1999) Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization, Applied Optics, vol. 38(18), 3831-3843.

Lee Z, Carder KL, Mobley CD, Steward RG, Patch JS (1998) Hyperspectral remote sensing for shallow waters. I. A semianalytical model, Applied Optics, vol. 37(27), 6329-6338.

Remote Sensing in the Cloud – Evaluating water optical properties using HICO IPS

This is part of an ongoing series dedicated to reviewing the algorithms currently implemented in the cloud-based HICO Image Processing System (HICO IPS). Links to additional posts in this series describing the other algorithms are provided below. Here we provide an overview of the algorithm utilized for evaluating water optical properties in coastal and oceanic water.

HICO IPS

Objective – Retrieve water optical properties for coastal and oceanic water from hyperspectral imagery using a generalized multi-band algorithm.

Algorithm – Estimate water optical properties for absorption and backscattering (specifically, total absorption, phytoplankton absorption, detritus and gelbstoff absorption, total backscattering, and particle backscattering) using the Quasi-Analytical Algorithm (QAA v5; Lee et al. 2009, 2002).

Inputs – User specified HICO scene, with optional region-of-interest; optional NDWI land/water mask, with user adjustable NDWI threshold; and specification of desired optical property.

HICO IPS Turkish Straits

Output – Selected water optical property at 438 nm (m-1) depicted using a blue-red color ramp where blue represents low values and red represents high values. If the NDWI land/water mask was selected, then the selected optical property is only calculated and mapped for the water pixels.

HICO IPS Turkish Straits QAA

Try it out today for yourself: http://hyspeedgeo.com/HICO/

 

Related posts

Introducing the HICO Image Processing System

Calculating a land/water mask using HICO IPS

Deriving chlorophyll concentration using HICO IPS

Characterizing shallow coastal environments using HICO IPS

 

References

Lee Z, Lubac B, Werdell J, Arnone R (2009) An update of the quasi-analytical algorithm (QAA_v5), International Ocean Color Group Software Report, 9 pp.

Lee Z, Carder KL, Arnone RA (2002) Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters, Applied optics, vol. 41(27), 5755-5772.

Remote Sensing in the Cloud – Deriving chlorophyll concentration using HICO IPS

Continuing our review of the algorithms currently implemented in the cloud-based HICO Image Processing System (HICO IPS), here we provide an overview of the two algorithms utilized for deriving estimates of chlorophyll concentration in oceanic water.

HICO IPS

Objective – Implement multiple algorithms for estimating chlorophyll concentration, as well as a methodology for evaluating the difference between these algorithms.

Algorithms – Derive estimates of surface chlorophyll-a concentration using one of two ocean color algorithms, OC4 (O’Reilly et al. 2000) or OCI (Hu et al. 2012); or for comparison, the difference between these two algorithms (OC4 – OCI). These algorithms first perform spectral resampling of the HICO hyperspectral data to the multispectral SeaWiFS bands on which the algorithms are based.

Inputs – User specified HICO scene, with optional region-of-interest; optional NDWI land/water mask, with user adjustable NDWI threshold; and specification of desired chlorophyll algorithm.

HICO IPS Chesapeake Bay

Output – Surface chlorophyll-a concentration (mg/m^3) depicted using a blue-red color ramp where blue represents low chlorophyll concentration and red represents high concentration. If the NDWI land/water mask was selected, then chlorophyll concentrations are only calculated and mapped for the water pixels (as is logical).

HICO IPS Chesapeake Bay Chlorophyll

Try it out today for yourself: http://hyspeedgeo.com/HICO/

 

Related posts

Introducing the HICO Image Processing System

Calculating a land/water mask using HICO IPS

Evaluating water optical properties using HICO IPS

Characterizing shallow coastal environments using HICO IPS

 

References

Hu C, Lee Z, Franz BA (2012) Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, Journal of Geophysical Research, vol. 117(C1), 25 pp.

O’Reilly JE, Maritorena S, O’Brien MC, et al. (2000) SeaWiFS postlaunch calibration and validation analyses, Part 3, NASA Technical Memorandum 2000-206892, vol. 11, 49 pp.

Remote Sensing in the Cloud – Calculating a land/water mask using HICO IPS

Last year we launched the HICO Image Processing System (HICO IPS) – a prototype web application for on-demand remote sensing data analysis in the cloud.

HICO IPS

To demonstrate the capabilities of this system, we implemented a collection of coastal remote sensing algorithms to produce information on water quality, water depth and benthic features using example imagery from the HICO instrument on the International Space Station.

As the HICO IPS approaches its one year anniversary, and continues its excellent performance, we’d like to take a moment to highlight each of the algorithms currently implemented in the system.

Here we begin with an overview of the land/water mask utilized in the HICO IPS.

Objective – Implement an automated algorithm for classifying land versus water, thereby masking land pixels from further analysis and allowing subsequent processing steps to focus on just water pixels.

Algorithm – Generates a binary mask differentiating land from water using the Normalized Difference Water Index (NDWI; McFeeters 1996). This algorithm can be implemented on its own, or as a pre-processing step in other algorithm workflows.

Inputs – User specified HICO scene, with optional region-of-interest; and user adjustable NDWI threshold, where -1.0 ≤ NDWI ≤ 1.0, land ≤ threshold < water, and default threshold = 0.0.

HICO IPS Christchurch

Output – Binary land/water mask (0 = land; 1 = water), where land is displayed in the online map using a black mask and water remains unchanged.

HICO IPS Christchurch mask

Reference – McFeeters SK (1996) The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, International Journal of Remote Sensing, vol. 17(7), 1425-1432.

Try it out today for yourself: http://hyspeedgeo.com/HICO/

 

Related posts

Introducing the HICO Image Processing System

Deriving chlorophyll concentration using HICO IPS

Evaluating water optical properties using HICO IPS

Characterizing shallow coastal environments using HICO IPS

From here to there – and everywhere – with Geospatial Cloud Computing

Reposted from Exelis VIS, Imagery Speaks, June 30, 2015, by James Goodman, CEO HySpeed Computing.

In a previous article we presented an overview of the advantages of cloud computing in remote sensing applications, and described an upcoming prototype web application for processing imagery from the HICO sensor on the International Space Station.

First, as a follow up, we’re excited to announce availability of the HICO Image Processing System – a cloud computing platform for on-demand remote sensing image analysis and data visualization.

HICO IPS - Chesapeake Bay - Chlorophyll

HICO IPS allows users to select specific images and algorithms, dynamically launch analysis routines in the cloud, and then see results displayed directly in an online map interface. System capabilities are demonstrated using imagery collected by the Hyperspectral Imager for the Coastal Ocean (HICO) on the International Space Station, and example algorithms are included for assessing coastal water quality and other nearshore environmental conditions.

This is an application-server, and not just a map-server. Thus, HICO IPS is delivering on-demand image processing of real physical parameters, such as chlorophyll concentration, inherent optical properties, and water depth.

The system was developed using a combination of commercial and open-source software, with core image processing performed using the recently released ENVI Services Engine. No specialized software is required to run HICO IPS. You just need an internet connection and a web browser to run the application (we suggest using Google Chrome).

Beyond HICO, and beyond the coastal ocean, the system can be configured for any number of different remote sensing instruments and applications, thus providing an adaptable cloud computing framework for rapidly implementing new algorithms and applications, as well as making these applications and their output readily available to the global user community.

However, this is but one application. Significantly greater work is needed throughout the remote sensing community to leverage these and other exciting new tools and processing capabilities. To participate in a discussion of how the future of geospatial image processing is evolving, and see a presentation of the HICO IPS, join us at the upcoming ENVI Analytics Symposium in Boulder, CO, August 25-26.

With this broader context in mind, and as a second follow-up, we ask the important question when envisioning this future of how we as an industry, and as a research community, are going to get from here to there?

The currently expanding diversity and volume of remote sensing data presents particular challenges for aggregating data relevant to specific research applications, developing analysis tools that can be extended to a variety of sensors, efficiently implementing data processing across a distributed storage network, and delivering value-added products to a broad range of stakeholders.

Based on lessons learned from developing the HICO IPS, here we identify three important requirements needed to meet these challenges:

  • Data and application interoperability need to continue evolving. This need speaks to the use of broadly accessible data formats, expansion of software binding libraries, and development of cross-platform applications.
  • Improved mechanisms are needed for transforming research achievements into functional software applications. Greater impact can be achieved, larger audiences reached, and application opportunities significantly enhanced, if more investment is made in remote sensing technology transfer.
  • Robust tools are required for decision support and information delivery. This requirement necessitates development of intuitive visualization and user interface tools that will assist users in understanding image analysis output products as well as contribute to more informed decision making.

These developments will not happen overnight, but the pace of the industry indicates that such transformations are already in process and that geospatial image processing will continue to evolve at a rapid rate. We encourage you to participate.

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.

To access the HICO Image Processing System: http://hyspeedgeo.com/HICO/

HICO Image Gallery – Looking beyond the data

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What’s in an image? Beyond the visual impact, beyond the pixels, and beyond the data, there’s valuable information to be had. It just takes the right tools to extract that information.

With that thought in mind, HySpeed Computing created the HICO Image Processing System to make these tools readily available and thereby put image processing capabilities directly in your hands.

The HICO IPS is a prototype web application for on-demand remote sensing image analysis in the cloud. It’s available through your browser, so it doesn’t require any specialized software, and you don’t have to be a remote sensing expert to use the system.

HICO, the Hyperspectral Imager for the Coastal Ocean, operating on the International Space Station from 2009-2014, is the first space-based imaging spectrometer designed specifically to measure the coastal environment. And research shows that substantial amounts of information can be derived from this imagery.

To commemorate the recent launch of the HICO IPS and celebrate the beauty of our coastal environment, we’ve put together a gallery highlighting some of the stunning images acquired by HICO that are available in the system.

We hope you enjoy the images, and encourage you to explore the HICO IPS web application to try out your own remote sensing analysis.

HICO IPS: Chesapeake Bay Chla

To access the HICO Image Processing System: http://hyspeedgeo.com/HICO/

For more information on HICO: http://hico.coas.oregonstate.edu/

Remote Sensing Analysis in the Cloud – Introducing the HICO Image Processing System

HySpeed Computing is pleased to announce release of the HICO Image Processing System – a prototype web application for on-demand remote sensing image analysis in the cloud.

HICO IPS: Chesapeake Bay Chla

What is the HICO Image Processing System?

The HICO IPS is an interactive web-application that allows users to specify image and algorithm selections, dynamically launch analysis routines in the cloud, and then see results displayed directly in the map interface.

The system capabilities are demonstrated using imagery collected by the Hyperspectral Imager for the Coastal Ocean (HICO) located on the International Space Station, and example algorithms are included for assessing coastal water quality and other nearshore environmental conditions.

What is needed to run the HICO IPS?

No specialized software is required. You just need an internet connection and a web browser to run the application (we suggest using Google Chrome).

How is this different than online map services?

This is an application-server, not a map-server, so all the results you see are dynamically generated on-demand at your request. It’s remote sensing image analysis in the cloud.

What software was used to create the HICO IPS?

The HICO IPS is a combination of commercial and open-source software; with core image processing performed using the recently released ENVI Services Engine.

What are some of the advantages of this system?

The system can be configured for any number of different remote sensing instruments and applications, thus providing an adaptable framework for rapidly implementing new algorithms and applications, as well as making these applications and their output readily available to the global user community.

Try it out today and let us know what you think: http://hyspeedgeo.com/HICO/

 

Related posts

Calculating a land/water mask using HICO IPS

Deriving chlorophyll concentration using HICO IPS

Evaluating water optical properties using HICO IPS

Characterizing shallow coastal environments using HICO IPS

Geospatial Solutions in the Cloud

Source: Exelis VIS whitepaper – 12/2/2014 (reprinted with permission)

What are Geospatial Analytics?

Geospatial analytics allow people to ask questions of data that exist within a spatial context. Usually this means extracting information from remotely sensed data such as multispectral imagery or LiDAR that is focused on observing the Earth and the things happening on it, both in a static sense or over a period of time. Familiar examples of this type of geospatial analysis include Land Classification, Change Detection, Soil and Vegetative indexes, and depending on the bands of your data, Target Detection and Material Identification. However, geospatial analytics can also mean analyzing data that is not optical in nature.

So what other types of problems can geospatial analytics solve? Geospatial analytics comprise of more than just images laid over a representation of the Earth. Geospatial analytics can ask questions of ANY type of geospatial data, and provide insight into static and changing conditions within a multi-dimensional space. Things like aircraft vectors in space and time, wind speeds, or ocean currents can be introduced into geospatial algorithms to provide more context to a problem and to enable new correlations to be made between variables.

Many times, advanced analytics like these can benefit from the power of cloud, or server-based computing. Benefits from the implementation of cloud-based geospatial analytics include the ability to serve on-demand analytic requests from connected devices, run complex algorithms on large datasets, or perform continuous analysis on a series of changing variables. Cloud analytics also improve the ability to conduct multi-modal analysis, or processes that take into account many different types of geospatial information.

Here we can see vectors of a UAV along with the ground footprint of the sensor overlaid in Google Earth™, as well as a custom interface built on ENVI that allows users to visualize real-time weather data in four dimensions (Figure 1).

geospatial_cloud_fig1

Figure 1 – Multi-Modal Geospatial Analysis – data courtesy NOAA

These are just a few examples of non-traditional geospatial analytics that cloud-based architecture is very good at solving.

Cloud-Based Geospatial Analysis Models 

So let’s take a quick look at how cloud-based analytics work. There are two different operational models for running analytics, the on-demand model and the batch process model. In a on-demand model (Figure 2), a user generally requests a specific piece of information from a web-enabled device such as a computer, a tablet, or a smart phone. Here the user is making the request to a cloud based resource.

geospatial_cloud_fig2

Figure 2 – On-Demand Analysis Model

Next, the server identifies the requested data and runs the selected analysis on it. This leverages scalable server architecture that can vastly decrease the amount of time it takes to run the analysis and eliminate the need to host the data or the software on the web-enabled device. Finally, the requested information is sent back to the user, usually at a fraction of the bandwidth cost required to move large amounts of data or full resolution derived products through the internet.

In the automated batch process analysis model (Figure 3), the cloud is designed to conduct prescribed analysis to data as it becomes available to the system, reducing the amount of manual interaction and time that it takes to prepare or analyze data. This system can take in huge volumes of data from various sources such as aerial or satellite images, vector data, full motion video, radar, or other data types, and then run a set of pre-determined analyses on that data depending on the data type and the requested information.

geospatial_cloud_fig3

Figure 3 – Automated Batch Process Model

Once the data has been pre-processed, it is ready for consumption, and the information is pushed out to either another cloud based asset, such as an individual user that needs to request information or monitor assets in real-time, or simply placed into a database in a ‘ready-state’ to be accessed and analyzed later.

The ability for this type of system to leverage the computing power of scalable server stacks enables the processing of huge amounts of data and greatly reduces the time and resources needed to get raw data into a consumable state.

Solutions in the Cloud

HySpeed Computing

Now let’s take a look at a couple of use cases that employ ENVI capabilities in the cloud. The first is a web-based interface that allows users to perform on-demand geospatial analytics on hyperspectral data supplied by HICO™, the Hyperspectral Imager for the Coastal Ocean (Figure 4). HICO is a hyperspectral imaging spectrometer that is attached to the International Space Station (ISS) and is designed specifically for sampling the coastal ocean in an effort to further our understanding of the world’s coastal regions.

geospatial_cloud_fig4

Figure 4 – The HICO Sensor – image courtesy of NASA

Developed by HySpeed Computing, the prototype HICO Image Processing System (Figure 5) allows users to conduct on-demand image analysis of HICO’s imagery from a web-based browser through the use of ENVI cloud capabilities.

geospatial_cloud_fig5

Figure 5 – The HICO Image Processing System – data courtesy of NASA

The interface exposes several custom ENVI tasks designed specifically to take advantage of the unique spectral resolution of the HICO sensor to extract information characterizing the coastal environment. This type of interface is a good example of the on-demand scenario presented earlier, as it allows users to conduct on-demand analysis in the cloud without the need to have direct access to the data or the computing power to run the hyperspectral algorithms.

The goal of this system is to provide ubiquitous access to the robust HICO catalog of hyperspectral data as well as the ENVI algorithms needed to analyze them. This will allow researchers and other analysts the ability to conduct valuable coastal research using web-based interfaces while capitalizing on the efforts of the Office of Naval Research, NASA, and Oregon State University that went into the development, deployment, and operation of HICO.

Milcord

Another use case involves a real-time analysis scenario that comes from a company called Milcord and their dPlan Next Generation Mission Manager (Figure 6). The goal of dPlan is to “aid mission managers by employing an intelligent, real-time decision engine for multi-vehicle operations and re-planning tasks” [1]. What this means is that dPlan helps folks make UAV flight plans based upon a number of different dynamic factors, and delivers the best plan for multiple assets both before and during the actual operation.

geospatial_cloud_fig6

Figure 6 – The dPlan Next Generation Mission Manager

Factors that are used to help score the flight plans include fuel availability, schedule metrics based upon priorities for each target, as well as what are known as National Image Interpretability Rating Scales, or NIIRS (Figure 7). NIIRS are used “to define and measure the quality of images and performance of imaging systems. Through a process referred to as “rating” an image, the NIIRS is used by imagery analysts to assign a number which indicates the interpretability of a given image.” [2]

geospatial_cloud_fig7

Figure 7 – Extent of NIIRS 1-9 Grids Centered in an Area Near Calgary

These factors are combined into a cost function, and dPlan uses the cost function to find the optimal flight plan for multiple assets over a multitude of targets. dPlan also performs a cost-benefit analysis to indicate if the asset cannot reach all targets, and which target might be the lowest cost to remove from the plan, or whether another asset can visit the target instead.

dPlan employs a custom ESE application to generate huge grids of Line of Sight values and NIIRs values associated with a given asset and target (Figure 8). dPlan uses this grid of points to generate route geometry, for example, how close and at what angle does the asset need to approach the target.

geospatial_cloud_fig8

Figure 8 – dPlan NIIRS Workflow

The cloud-computing power leveraged by dPlan allows users to re-evaluate flight plans on the fly, taking into account new information as it becomes available in real time. dPlan is a great example of how cloud-based computing combined with powerful analysis algorithms can solve complex problems in real time and reduce the resources needed to make accurate decisions amidst changing environments.

Solutions in the Cloud

So what do we do here at Exelis to enable folks like HySpeed Computing and Milcord to ask these kinds of questions from their data and retrieve reliable answers? The technology they’re using is called the ENVI Services Engine (Figure 9), an enterprise-ready version of the ENVI image analytics stack. We currently have over 60 out-of-the-box analysis tasks built into it, and are creating more with every release.

geospatial_cloud_fig9

Figure 9 – The ENVI Services Engine

The real value here is that ENVI Services Engine allows users to develop their own analysis tasks and expose them through the engine. This is what enables users to develop unique solutions to geospatial problems and share them as repeatable processes for others to use. These solutions can be run over and over again on different data and provide consistent dependable information to the persons requesting the analysis. The cloud based technology makes it easy to access from web-enabled devices while leveraging the enormous computing power of scalable server instances. This combination of customizable geospatial analysis tasks and virtually limitless computing power begins to address some of the limiting factors of analyzing what is known as big data, or datasets so large and complex that traditional computing practices are not sufficient to identify correlations within disconnected data streams.

Our goal here at Exelis is to enable you to develop custom solutions to industry-specific geospatial problems using interoperable, off-the-shelf technology. For more information on what we can do for you and your organization, please feel free to contact us.

Sources:

[1] 2014. Milcord. “Geospatial Analytics in the Cloud: Successful Application Scenarios” webinar. https://event.webcasts.com/starthere.jsp?ei=1042556

[2] 2014. The Federation of American Scientists. “National Image Interpretability Rating Scales”. http://fas.org/irp/imint/niirs.htm

 

Advantages of Cloud Computing in Remote Sensing Applications

The original version of this post appears in the June 26 edition of Exelis VIS’s Imagery Speaks, by James Goodman, CEO HySpeed Computing

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?

  • Provides online, on-demand, scalable image processing capabilities.
  • Delivers image-derived products and visualization tools to a global user community.
  • Allows processing tools to be efficiently co-located with large image databases.
  • Removes software barriers and hardware requirements from non-specialists.
  • Facilitates rapid integration and deployment of new algorithms and processing tools.
  • Accelerates technology transfer in remote sensing through improved application sharing.
  • Connects remote sensing scientists more directly with the intended end-users.

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.