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Earth Observation in the Cloud using ENVIBeau Legeer

harris.com

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AgendaENVIs goal with Earth Observation in the CloudMoving from File->Open to Search and DiscoverA world of analytics in a web browserThere is an app for thatWhere can we go next..

Sentinel-2A image data courtesy of European Space Agency

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ENVIs goals for Earth Observation in the CloudTransform the users experience from File->Open to Search and Discover

Expand the amount of data that can be brought to focused problems through cloud data sources

Use cloud based data archives to derive data trends not possible through local data

Expand the addressable size of imagery based analytics using clouds

Deploy analytics to the connected world

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Making the move from File->OpenUse remote connection support to open data OGC, S3, etc. data sources.Use custom query tools to search / discover specific data sourcesExample: Landsat 8 Query ToolConnect to AWS Landsat ServiceSearch by date / timeCloud cover, Path/RowOpen in ENVI - exploit

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Consume ENVI from mobile, web, and thin clients

Access to all Landsat Data for exploitation in the ENVI Desktop

ENVI Desktop based analytics

Demo 1 Landsat Search, Discover, Exploit Data Source = AWS Landsat Service

Software = ENVI Desktop

Tasks Landsat Query Tool ISOData classification Classification Smoothing

A World of Analytics in a Web Browser

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Consume ENVI from mobile, web, and thin clients

Once you have configured your architecture and created image analysis services to be served via the web, you can then embed the ability to call your servers functionality from your mobile app, web based viewer, or thin client. Functional calls can be made direct to ENVI Services via HTTP REST, or routed through your middleware via standards based specifications such as Open GeoSpatial Consortium OGC Web Services or Esris Open GeoServices REST Specification.

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A World of Analytics in a Web BrowserServer based analytics are a perfect match for AWS Cloud Based DataMove the analytics off the desktop to a neutral, but processing heavy locationAccess data as a service instead of locallyWeb Clients can provide map based search and discoveryThe details of the data are abstracted to area and time.The focus becomes the problem to be solved.Users focus solving on specific problemsAVIRIS image data courtesy of NASA Jet Propulsion Laboratory

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Consume ENVI from mobile, web, and thin clients

Change Detection of any Two Images in the Landsat Catalog

ENVI Web based analytics

Demo 2 Landsat Change Detection Data Source = AWS Landsat Service

Software = ENVI Services Engine

Tasks Image Intersection Radiometric Calibration Image Difference Auto Threshold Aggregration Smoothing

A World of Analytics in a Web Browser

Harris Proprietary Information

Consume ENVI from mobile, web, and thin clients

Once you have configured your architecture and created image analysis services to be served via the web, you can then embed the ability to call your servers functionality from your mobile app, web based viewer, or thin client. Functional calls can be made direct to ENVI Services via HTTP REST, or routed through your middleware via standards based specifications such as Open GeoSpatial Consortium OGC Web Services or Esris Open GeoServices REST Specification.

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Consume ENVI from mobile, web, and thin clients

Online endmember definition and supervised classification

ENVI Web based analytics

Demo 3 Online Supervised Classification Data Source = AWS Landsat Service

Software = ENVI Services Engine

Tasks Spectral Angle Mapper Aggregation Smoothing Reprojection

Harris Proprietary Information

Consume ENVI from mobile, web, and thin clients

Once you have configured your architecture and created image analysis services to be served via the web, you can then embed the ability to call your servers functionality from your mobile app, web based viewer, or thin client. Functional calls can be made direct to ENVI Services via HTTP REST, or routed through your middleware via standards based specifications such as Open GeoSpatial Consortium OGC Web Services or Esris Open GeoServices REST Specification.

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Consume ENVI from mobile, web, and thin clients

Calculate Snow, Vegetation and other Spectral Indices from Online services

ENVI Web based analytics

Demo 4 Snow Coverage Data Source = AWS Landsat Service

Software = ENVI Services Engine

Tasks Spectral Index Snow Index Thresholding

Harris Proprietary Information

Consume ENVI from mobile, web, and thin clients

Once you have configured your architecture and created image analysis services to be served via the web, you can then embed the ability to call your servers functionality from your mobile app, web based viewer, or thin client. Functional calls can be made direct to ENVI Services via HTTP REST, or routed through your middleware via standards based specifications such as Open GeoSpatial Consortium OGC Web Services or Esris Open GeoServices REST Specification.

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There is an app for that!

harris.com

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Consume ENVI from mobile, web, and thin clients

Vegetation Monitoring on an iPhone powered by AWS Landsat

ENVI Web based analytics

Demo 5 iOS Veg Monitoring Data Source = AWS Landsat Service

Software = ENVI Services Engine, AppSymphony from Optensity

Tasks Spectral Indices

Harris Proprietary Information

Consume ENVI from mobile, web, and thin clients

Once you have configured your architecture and created image analysis services to be served via the web, you can then embed the ability to call your servers functionality from your mobile app, web based viewer, or thin client. Functional calls can be made direct to ENVI Services via HTTP REST, or routed through your middleware via standards based specifications such as Open GeoSpatial Consortium OGC Web Services or Esris Open GeoServices REST Specification.

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Consume ENVI from mobile, web, and thin clients

Vegetation Monitoring on an iPhone powered by AWS Landsat

ENVI Web based analytics

Demo 5 iOS Veg Monitoring Data Source = AWS Landsat Service

Software = ENVI Services Engine, AppSymphony from Optensity

Tasks Spectral Indices

Harris Proprietary Information

Consume ENVI from mobile, web, and thin clients

Once you have configured your architecture and created image analysis services to be served via the web, you can then embed the ability to call your servers functionality from your mobile app, web based viewer, or thin client. Functional calls can be made direct to ENVI Services via HTTP REST, or routed through your middleware via standards based specifications such as Open GeoSpatial Consortium OGC Web Services or Esris Open GeoServices REST Specification.

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Where do we go next?

harris.com

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Time, trends and large scale analysisServices providing data over long time rangesAnalytics trendingPerformance metrics for materialsLooking backwards to predict future performanceMultivariate analysis time, season, sensor, etc.Extending past performance forward.Large spatial areas using scalable clouds and cloud based data sourcesLandsat AWSDigitalGlobe Geospatial Big Data (GBD)

AVIRIS image data courtesy of NASA Jet Propulsion Laboratory

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15ENVI on DigitalGlobe GBD

+Software + Platform + Data + ExpertiseBoth Harris & DigitalGlobe can bring to market bundled offeringBoth sides believe in information extraction from imagery

DigitalGlobe Proprietary and Business Confidential

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Consume ENVI from mobile, web, and thin clients

Visualization of Spectral Signature Performance over 40 years of data.

ENVI Web based analytics

Demo 6 Time Signature Analysis Data Source = AWS Landsat Service

Software = ENVI Services Engine

Tasks Time Profile Tools

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Consume ENVI from mobile, web, and thin clients

Once you have configured your architecture and created image analysis services to be served via the web, you can then embed the ability to call your servers functionality from your mobile app, web based viewer, or thin client. Functional calls can be made direct to ENVI Services via HTTP REST, or routed through your middleware via standards based specifications such as Open GeoSpatial Consortium OGC Web Services or Esris Open GeoServices REST Specification.

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Thank You!Beau Legeer Harris Geospatialmlegeer@harris.com

AVIRIS image data courtesy of NASA Jet Propulsion Laboratory

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