Publications

Divis, Kristin; Chen, Maximillian G.; McNamara, Laura A.; Morrow, James D.; Perkins, David N.

Abstract not provided.

Chen, Maximillian G.; Divis, Kristin; McNamara, Laura A.; Morrow, James D.; Perkins, David N.

Abstract not provided.

Perkins, David N.; Biedermann, Laura B.; Clark, Blythe C.; Thayer, Rachel C.; Dagel, Amber L.; Gupta, Vipin P.; Hibbs, Michael R.; West, Roger D.

The goal of this SAND report is to provide guidance for other groups hosting workshops and peerto-peer learning events at Sandia. Thus this SAND report provides detail about our team structure, how we brainstormed workshop topics and developed the workshop structure. A Workshop “Nuts and Bolts” section provides our timeline and check-list for workshop activities. The survey section provides examples of the questions we asked and how we adapted the workshop in response to the feedback.

Coram, Jamie L.; Morrow, James D.; Perkins, David N.

This document describes the PANTHER R&D Application, a proof-of-concept user interface application developed under the PANTHER Grand Challenge LDRD. The purpose of the application is to explore interaction models for graph analytics, drive algorithmic improvements from an end-user point of view, and support demonstration of PANTHER technologies to potential customers. The R&D Application implements a graph-centric interaction model that exposes analysts to the algorithms contained within the GeoGraphy graph analytics library. Users define geospatial-temporal semantic graph queries by constructing search templates based on nodes, edges, and the constraints among them. Users then analyze the results of the queries using both geo-spatial and temporal visualizations. Development of this application has made user experience an explicit driver for project and algorithmic level decisions that will affect how analysts one day make use of PANTHER technologies.

Proceedings of SPIE - The International Society for Optical Engineering

Moya, Mary M.; Koch, Mark W.; Perkins, David N.; West, Roger D.

Sandia National Laboratories produces copious amounts of high-resolution, single-polarization Synthetic Aperture Radar (SAR) imagery, much more than available researchers and analysts can examine. Automating the recognition of terrains and structures in SAR imagery is highly desired. The optical image processing community has shown that superpixel segmentation (SPS) algorithms divide an image into small compact regions of similar intensity. Applying these SPS algorithms to optical images can reduce image complexity, enhance statistical characterization and improve segmentation and categorization of scene objects. SPS algorithms typically require high SNR (signal-to-noise-ratio) images to define segment boundaries accurately. Unfortunately, SAR imagery contains speckle, a product of coherent image formation, which complicates the extraction of superpixel segments and could preclude their use. Some researchers have developed modified SPS algorithms that discount speckle for application to SAR imagery. We apply two widely-used SPS algorithms to speckle-reduced SAR image products, both single SAR products and combinations of multiple SAR products, which include both single polarization and multi-polarization SAR images. To evaluate the quality of resulting superpixels, we compute research-standard segmentation quality measures on the match between superpixels and hand-labeled ground-truth, as well as statistical characterization of the radar-cross-section within each superpixel. Results of this quality analysis determine the best input/algorithm/parameter set for SAR imagery. Simple Linear Iterative Clustering provides faster computation time, superpixels that conform to scene-relevant structures, direct control of average superpixel size and more uniform superpixel sizes for improved statistical estimation which will facilitate subsequent terrain/structure categorization and segmentation into scene-relevant regions. © 2014 SPIE.

Brost, Randolph B.; Perkins, David N.

Abstract not provided.