2016 asprs track: science, scale, and innovation: when remote sensing analystics drive solutions in...

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SCIENCE, SCALE, & INNOVATION: REMOTE SENSING ANALYTICS DRIVING SOLUTIONS IN THE MODERN ENTERPRISE

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Remote Sensing & Satellite Imaging

From the Beginning:

First Satellite Image: U.S. Explorer VI - 1959

http://www.readwritethink.org/classroom-resources/calendar-activities/first-picture-earth-taken-20275.html


Why not just leave it at that?

Change | Growth | Progress | Necessity

invention

innovationNecessity is the mother of


Remote Sensing & Satellite Imaging

Remote Sensing Innovations


Consumable Remote Sensing Analytics

Science, Scale, and Analytics in the Modern Enterprise

Images to Information

Remote Sensing Analytics to Scalable & Consumable Solutions

for the Modern Enterprise


Outside our Industry:

Inspired Innovations (from Shark Tank)


Use Case: The CDC Grass Program Fight Against Zika

Science:• Multispectral analytics• Automated custom workflows

Scale:• Small pixels = high spatial resolution

Innovation:• Combining spectral indices in an automated workflow enable specialized analytics



Project Description:• Finding potential areas of mosquito larvae in areas where the Zika virus is prevalent is

paramount to launching a fight against its spread.• High resolution airborne imagery with low spectral resolution was acquired with the goal of

not only finding water, but finding water where larvae may be present.• Many of the pools are at abandoned properties, water with algae provide favorable

conditions for larvae to grow.

4-band image courtesy of CDC GRASS program



Image highlighting pools (bright blue)

Solution:

• A simple spectral index was used to identify and delineate the pools.

• The goal is to find which pools are anomalous compared to the other pools



Solution (continued):

Spectral indices were combined.

Anomalous pool highlighted based on NDBGI overall score.

GIS database used to cross-reference addresses

Mitigation crew can target locations for water treatment


Use Case: Remote Sensing Data to Business Intelligence

Science:• Feature Extraction• Temporal Analytics• Technology Integration

Scale:• Large stack of multi-temporal information

Innovation:• Machine Learning/ Machine Vision • Satellite Image -> Information• GIS/Remote Sensing + New technologies from other business sectors



Project Description:• The goal is to gain business intelligence about change over time (example uses aircraft).• In order to do that, planes and traffic needed to be located (feature extraction) and change

analysis needed to per performed over time.• There should be no user interaction until the final dashboards are viewed and interpreted.• The input data include a temporal stack of WorldView-2 panchromatic images. • Automated Feature Extracted using machine learning technology was used.



Solution:• Features are found and a postgres database is populated with the components.• This happens in a completely automated way – the user does not see this intermediate step.



Solution (continued):• A slippy map is served to deliver some context of the features found. This is available to the

user but no interaction is required at this step.



Solution (continued):• The database is used to automatically populate Elasticsearch, which is then used to serve

information to Kibana – which is used to build dashboards.• Important point: The data visualizations required for data interpretation are NOT images nor

geospatial features.


Use Case: 3D from Space for Disaster Response

Science:• Extracting 3D information from space-borne input• Comparing multi-modal information (not apples-to-apples)

Scale:• Leverage modern infrastructure for computationally intensive

processes.

Innovation:• Accurately measuring ground features from space• Enabling time-sensitive analytics



Project Description:• A landslide occurs in Malin, India – prioritizing

response efforts must be accomplished in a short amount of time.

• The solution must include the ability to characterize, qualify, and quantify the damage.

• Satellite imagery is used “after” the event.• A high resolution DEM is used for the “before”

image.

Original stereo-pair Worldview-2 satellite imagery courtesy of Digital Globe



Solution:• Stereo-photogrammetric processing of satellite imagery is used

to generate a 3D model of the event & surrounding area.

3D digital surface model generated from stereo-pair using the ENVI Phtoogrammetry Module

Digital Elevation Model (DEM) shaded by height



Solution (continued):• A high resolution DEM “before” the event was used as a

comparison reference. However the resolution at ~10m was significantly different than the “after” resolution.

World DEM courtesy of Airbus Defense and Space ~10m spatial resolution

Stereo-satellite derived DEM colored by height



Solution (continued):• A simple subtraction “after – before” does not give a

clear picture of the areas requiring priority attention. Instead, advanced image analytics are used to highlight the areas most in-need of immediate action.

Simple subtraction of after from before results in noisy result where high risk areas are unclear.

Difference after from before with filters applied to keep relevant information and remove noise.


Use Case: Precision Agriculture in Cloud-based Infrastructure

Science:• Custom analytics to identify plants with thick canopy• Determining statistically significant anomalies for

crops

Scale:• Algorithms must work on spatial scale (strawberries

vs. trees)• Must scale to thousands of acres in reasonable

amount of time

Innovation:• Web-based interface that keeps the “complexity” on

the back-end• New algorithms specific to new data (UAVs, crops)


Use Case: Precision Agriculture in Cloud-based Infrastructure

Project Description:• A precision farming pilot project has a need to run image analytics from an easy-to-

use web-based client that enables standard and custom processes to run and produce reports.

• The analytics must include the ability to gain insight at the “per plant” level, meaning plant counting and identification are part of the solution.

• The user should be able to gain insight into the absolute or relative health of each plant.

• The information from the processing will be used to determine the amount of water and/or fertilizer to apply to each plant.

• The end-user does not have knowledge of remote sensing or computer science.


Use Case: Precision Ag Toolkit

Solution:

• A dashboard was built as a thin client interface to ENVI Analytics and is running on the Amazon Cloud

Workflow from the dashboard:

• Navigate to a folder of data• Select the desired analytic(s)• Click “Go”• Download report



Above: Original image: 4-band Airbus imageData pre-processing: Calibrated, Pan Sharpened, Ortho, AC

Top Right: Intermediate pre-processed imageBottom Right: Features (plants) identified



Above: Output – healthy plants identified with color legend

Right: Output reports (examples)


Summary

Science, Scale, & Innovation are: • Driving advances in industries all around us, from cleaning our cell phones to making

sure our growing population has enough food, etc…• Within our industry, these same necessities of water, food, safety, are driving us to

similarly innovate resulting in technologies that are enabling us to apply the science we know to solve the problems of tomorrow.

Henry Ford