1) The document discusses moving earth observation analytics to the cloud using ENVI software. This allows users to search, discover, and analyze satellite imagery directly in a web browser or mobile app instead of downloading files. 2) Examples are given of using cloud-based Landsat imagery with ENVI to do tasks like change detection, classification, and calculating indices like snow coverage. 3) The goals are to make more data accessible for analysis, perform analytics at scale in the cloud, and consume ENVI through web and mobile apps. This could enable large-scale analysis over time and across large regions using cloud data sources.

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Earth Observation in the
Cloud using ENVI
Beau Legeer

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Agenda
• ENVI’s goal with Earth Observation in
the Cloud
• Moving from File->Open to Search and
Discover
• A world of analytics in a web browser
• There is an app for that…
• Where can we go next..
Sentinel-2A image data courtesy of European Space Agency

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ENVI’s goals for Earth Observation in the Cloud
• Transform 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->Open
• Use remote connection support to open data
OGC, S3, etc. data sources.
• Use custom query tools to search / discover
specific data sources
• Example: Landsat 8 Query Tool
– Connect to AWS Landsat Service
– Search by date / time
– Cloud cover, Path/Row
– Open in ENVI - exploit

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Consume ENVI from
mobile, web, and thin
clients
Access toall Landsat Data for
exploitation inthe 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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A World of Analytics in a Web Browser
• Server based analytics are a perfect
match for AWS Cloud Based Data
– Move the analytics off the desktop to a
neutral, but processing heavy location
– Access data as a service instead of
locally
• Web Clients can provide map based
search and discovery
– The details of the data are abstracted to
area and time.
– The focus becomes the problem to be
solved.
• Users focus solving on specific problems
AVIRIS image data courtesy of NASA Jet Propulsion Laboratory

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Consume ENVI from
mobile, web, and thin
clients
Change Detection of any TwoImages
inthe 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

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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

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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

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There is an app for that!

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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

12. Harris Proprietary Information
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

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Where do we go next?

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Time, trends and large scale analysis
• Services providing data over long time
ranges
– Analytics trending
– Performance metrics for materials
• Looking backwards to predict future
performance
– Multivariate analysis – time, season, sensor,
etc.
– Extending past performance forward.
• Large spatial areas using scalable clouds and
cloud based data sources
– Landsat AWS
– DigitalGlobe Geospatial Big Data (GBD)
AVIRIS image data courtesy of NASA Jet Propulsion Laboratory

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DigitalGlobe Proprietary and Business Confidential15
ENVI on DigitalGlobe GBD
+
• Software + Platform + Data + Expertise
• Both Harris & DigitalGlobe can bring to market bundled offering
• Both sides believe in information extraction from imagery

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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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Thank You!
Beau Legeer – Harris Geospatial
mlegeer@harris.com
AVIRIS image data courtesy of NASA Jet Propulsion Laboratory