Mr. James Rick Profile

James Rick

at Georgia Tech Research Institute

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 10 May 2019 Paper

Reducing bathymetric-lidar algorithm uncertainty with genetic programming and the evolutionary multi-objective algorithm design engine

Jason Zutty, Domenic Carr, Rodd Talebi, James Rick, Christopher Valenta, Gregory Rohling

Proceedings Volume 11006, 110061U (2019) https://doi.org/10.1117/12.2519320

KEYWORDS: LIDAR, Data modeling, Computer programming, Machine learning, Algorithm development, Optimization (mathematics), Evolutionary algorithms, Computer simulations, Genetic algorithms

Read Abstract +

In recent years, the field of automated machine learning (autoML) has quickly attracted significant attention both in academia and industry. The driving force is to reduce the amount of human intervention required to process data and create models for classification and prediction, a tedious and arbitrary process for data scientists that may not often result in achieving a global optimum with respect to multiple objectives. Moreover, existing autoML techniques rely on extremely large collections of relatively clean training data, which is not typical of Multi-Domain Battle (MDB) applications. In this paper, we describe a methodology to optimize underwater seafloor detection for airborne bathymetric lidar, an application domain with sparse truth data, leveraging evolutionary algorithms and genetic programming. Our methodology uses the Evolutionary Multi-objective Algorithm Design Engine (EMADE) and a radiometric waveform simulator generating millions of waveforms from which genetic programming techniques select optimal signal processing techniques and their parameters given the goal of reducing Total Propagated Uncertainty (TPU). The EMADE affords several benefits not found in other autoML solutions, including the ability to stack machine learning models, process time-series data using dozens of signal-processing techniques, and efficiently evaluate algorithms on multiple objectives. Given the lack of truth data, we tune EMADE to produce detection algorithms that improve accuracy and reduce relevant measurement uncertainties for a wide variety of operational and environmental scenarios. Preliminary testing indicates successfully reducing TPU and reducing over- and under-prediction errors by 13.8% and 68.2% respectively, foreshadowing using EMADE to assist in future MDB-application algorithm development.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years