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Image Processing from UAS Sensor Data: Prospects and Challenges*

Robert J. (Bob) Jannarone, Gregory Schaefer, and Ciani Sparks, Brainlike, Inc.

Darren Ireland, Kathleen Leonard, and Dale Funk,

LGL Alaska Research Associates

* Some results are based on images that were kindly supplied by Resource Management, LLC and Shell Exploration & Science.

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Presentation Roadmap

1) Wildlife Detection Scenario 2) Added Value Analysis : two examples 3) Operational Prospects 4) Product Operation: a peak under the hood

(time permitting)

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Can you find caribou in this image?

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Can you find them in these “chips?”

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Can you identify them in these chips?

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Caribou Image: detail location

Area of Detail (next slide)

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Caribou Image: detail

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Caribou Detection: “alert map”

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Can you find whales in these images?

A.

B.

C.

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Can you find them in these image chips?

A.

B.

C.

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Can you identify them in these chips?

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Whale Image “Alert Map”

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Independent Validation (LGL et. al: Society for Marine Mammalogy 2015)

“Testing of the detection software occurred on images from surveys that contained tens-of-thousands of images without whales as well as tens of images with whales that had been withheld from the detector development phase. The overall successful detection rate was approximately 70%, with 100% of whales being detected on many surveys. The time required to manually review the automated processing output was less than 2% of the full manual analysis time.

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Presentation Roadmap

1) Wildlife Detection Scenario 2) Added Value Analysis: two examples 3) Operational Prospects 4) Product Operation: a peak under the hood

(time permitting)

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Effort Reduction Example (36 Mpix RGB)

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Continuous Monitoring Example: monetized benefit analysis:

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Monetized Benefit Analysis Detail

Likelihoods

Component Costs

Overall Risk/Utility

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Presentation Roadmap

1) Wildlife Detection Scenario 2) Added Value Analysis: two examples 3) Operational Prospects 4) Product Operation: a peak under the hood

(time permitting)

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Triage Extended Definition

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3. (in data processing) The rapid reduction of massive data streams to actionable information.

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Whale Detection Triage (and beyond)

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2014 Product: • Locates most targets automatically • Reduces analysis time by over 90% • Processes images in near real-time

2014 Objective: Deliver an automated alternative to manual, post-flight methods for detecting marine mammals from airborne images in a cluttered environment

2015-16 Goals: • General-purpose image–based target detection • Fully automated in-flight/post-flight detection • On-board, real-time processing • Full resolution, real-time “chip” (VGA-sized sub-frame) transmission • Small packet size • Low SWaP profile

Payoffs: • Reduced analyst/operator effort • Real-time, high resolution chip display • Low transmission bandwidth • Low transmission power • High sensor persistence • Low development/integration cost • Big return on investment

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On-Board Triage: UAS “Vision”

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High Resolution

Sensors

A P I

D

r i v e r

Triage

Processor Low Bandwidth Information

High Bandwidth

Data

Telemetry Control

Sensor Control

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Presentation Roadmap

1) Wildlife Detection Scenario 2) Added Value Analysis: two examples 3) Operational Prospects 4) Product Operation: a peak under the hood

(time permitting)

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Data Layout

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Shortcut

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Output Structure, 1

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Output Structure, 2

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Bat File Structure

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Whale Templates: two simple examples

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Template Matching: rotation example

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Template Matching: rescaling example

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Caribou Template Matching

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Caribou Template Matching

(cont.)

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Presentation Summary

1) Wildlife Detection Scenario 2) Added Value Analysis: two examples 3) Operational Prospects 4) Product Operation: a peak under the hood

(time permitting)

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Thank you very much for your interest!

Robert J. (Bob) Jannarone bobjannarone@brainlike.com

(619) 887-1153 (cell)