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523 results on '"Brown, Scott D."'

176. Time-Gated Topographic LIDAR Scene Simulation

Author

ROCHESTER INST OF TECH NY CHESTER F CARLSON CENTER FOR IMAGING SCIENCE, Brown, Scott D., Blevins, Daniel D., Schott, John R., ROCHESTER INST OF TECH NY CHESTER F CARLSON CENTER FOR IMAGING SCIENCE, Brown, Scott D., Blevins, Daniel D., and Schott, John R.

Abstract

The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model has been developed at the Rochester Institute of Technology (RIT) for over a decade. The model is an established, first-principles based scene simulation tool that has been focused on passive multi- and hyper-spectral sensing from the visible to long wave infrared (0.4 to 14 mum). Leveraging photon mapping techniques utilized by the computer graphics community, a first-principles based elastic Light Detection and Ranging (LIDAR) model was incorporated into the passive radiometry framework so that the model calculates arbitrary, time-gated radiances reaching the sensor for both the atmospheric and topographic returns. The active LIDAR module handles a wide variety of complicated scene geometries, a diverse set of surface and participating media optical characteristics, multiple bounce and multiple scattering effects, and a flexible suite of sensor models. This paper will present the numerical approaches employed to predict sensor reaching radiances and comparisons with analytically predicted results. Representative data sets generated by the DIRSIG model for a topographical LIDAR will be shown. Additionally, the results from phenomenological case studies including standard terrain topography, forest canopy penetration, and camouflaged hard targets will be presented.

Published
2002

186. Surface and Buried Landmine Scene Generation and Validation Using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) Model

Author

ROCHESTER INST OF TECH NY, Peterson, Erin D., Brown, Scott D., Hattenberger, Timothy J., Schott, John R., ROCHESTER INST OF TECH NY, Peterson, Erin D., Brown, Scott D., Hattenberger, Timothy J., and Schott, John R.

Abstract

Detection and neutralization of surface-laid and buried landmines has been a slow and dangerous endeavor for military forces and humanitarian organizations throughout the world. In an effort to make the process faster and safer, scientists have begun to exploit the ever-evolving passive electro-optical realm, both from a broadband perspective and a multi or hyperspectral perspective. Carried with this exploitation is the development of mine detection algorithms that take advantage of spectral features exhibited by mine targets, only available in a multi or hyperspectral data set. Difficulty in algorithm development arises from a lack of robust data, which is needed to appropriately test the validity of an algorithm's results. This paper discusses the development of synthetic data using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. A synthetic landmine scene has been modeled after data collected on the US Army's Yuma Proving Grounds by the University of Hawaii's Airborne Hyperspectral Imager (AHI). The synthetic data has been created and validated to represent the surrogate minefield thermally, spatially, spectrally, and temporally over the 7.9 to 11.5 micron region using 70 bands of data. Validation of the scene has been accomplished by direct comparison to the AHI truth data using qualitative band to band visual analysis, Rank Order Correlation comparison, Principle Components dimensionality analysis, and an evaluation of the R(x) algorithm's performance. This paper discusses landmine detection phenomenology, describes the steps taken to build the scene, methods utilized to overcome limitations of less than adequate ground truth, and compares the synthetic scene to truth data.

Published
2000

191. The Fragile Nature of Contextual Preference Reversals: Reply to Tsetsos, Chater, and Usher (2015).

Author

Trueblood, Jennifer S., Brown, Scott D., and Heathcote, Andrew

Subjects
*CONSUMER preferences, *MULTIPLE criteria decision making, *CHOICE (Psychology), *HUMAN behavior, *SIMILARITY (Psychology), *DYNAMIC models
Abstract

Trueblood, Brown, and Heathcote (2014) developed a new model, called the multiattribute linear ballistic accumulator (MLBA), to explain contextual preference reversals in multialtemative choice. MLBA was shown to provide good accounts of human behavior through both qualitative analyses and quantitative fitting of choice data. Tsetsos, Chater, and Usher (2015) investigated the ability of MLBA to simultaneously capture 3 prominent context effects (attraction, compromise, and similarity). They concluded that MLBA must set a "fine balance" of competing forces to account for all 3 effects simultaneously and that its predictions are sensitive to the position of the stimuli in the attribute space. Through a new experiment, we show that the 3 effects are very fragile and that only a small subset of people shows all 3 simultaneously. Thus, the predictions that Tsetsos et al. generated from the MLBA model turn out to match closely real data in a new experiment. Support for these predictions provides strong evidence for the MLBA. A corollary is that a model that can "robustly" capture all 3 effects simultaneously is not necessarily a good model. Rather, a good model captures patterns found in human data, but cannot accommodate patterns that are not found. [ABSTRACT FROM AUTHOR]

Published
2015
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