Publications

The CSISAR tool is GUI based and very simple to use. The algorithms are robust, and the unique processing flows, that the user is stepped through, virtually eliminate the possibility of error in GEOINT production. An integrated data manager is a key part of the CSISAR system. This data manager keeps track of the data available to a user and informs the user of what data is available and what can be done with that data. This keeps the user from having to be trained in the nuances of the algorithms. CSISAR also has an integrated product manager, which helps the user identify, view and manage previously made products. CSISAR was originally developed in 2010-2011 as a Windows based system. It was updated in 2015 to be a Linux based system. This SAND report is intended to make the Product Description and User’s Guide for CSISAR (originally included within the software) more widely available. New is a brief addition of Linux-specific installation details.

Abstract not provided.

Abstract not provided.

Single Image SICD-Based Automatic Object Processing (SIS-AOP) is an automatic object identification tool for SAR imagery. It ingests a SAR image in standard SICD format, and it will run a suite of algorithms to cue possible vehicle detections, cull those detections and then ultimately label them either as detections only or possible expound to give a class-level ID or a vehicle-type ID. The SIS-AOP results are given in an XML (Extensible Markup Language) output format. This document defines the elements in the SISAOPR XML output format.

For machine vision, one of the most important operations is fast and effective object cueing or segmentation. Sandia National Labs has a long history of development and implementation of very fast and effective cueing/segmentation algorithms. This report covers the history, motivation and implementation of evolving frameworks (Sandia FOA Frameworks) upon which this long legacy of successful algorithms are built. The report describes the innovative microprocessor implementation, enabling extremely fast morphological processing, combined with a novel adaptive quantization front - end and a feature - based backend that resulted in Sandia developing fast and effective cueing in a wide variety of applications, from defect detection to SAR ATR. The report covers evolution from Sandia FOA 1.0 Framework (1995) to current Sandia FOA 4.0 Framework (2021). Requirements for the cueing algorithm for SIS - AOP (FOA_SIS - AOP) that drove the Sandia FOA 4.0 Framework development are discussed, along with information on how to use the Sandia FOA Frameworks.

Phenomenology-Informed (PI) Machine Learning is introduced to address the unique challenges faced when applying modern machine-learning object recognition techniques to the SAR domain. PI-ML includes a collection of data normalization and augmentation techniques inspired by successful SAR ATR algorithms designed to bridge the gap between simulated and real-world SAR data for use in training Convolutional Neural Networks (CNNs) that perform well in the low-noise, feature-dense space of camera-based imagery. The efficacy of PI-ML will be evaluated using ResNet, EfficientNet, and other networks, using both traditional training techniques and all-SAR transfer learning.

Phenomenology-Informed (PI) Machine Learning is introduced to address the unique challenges faced when applying modern machine-learning object recognition techniques to the SAR domain. PI-ML includes a collection of data normalization and augmentation techniques inspired by successful SAR ATR algorithms designed to bridge the gap between simulated and real-world SAR data for use in training Convolutional Neural Networks (CNNs) that perform well in the low-noise, feature-dense space of camera-based imagery. The efficacy of PI-ML will be evaluated using ResNet, EfficientNet, and other networks, using both traditional training techniques and all-SAR transfer learning.

SAR imagery for coastline detection has many potential advantages over conventional optical stereoscopic techniques. For example, SAR does not have restrictions on being collected during daylight or when there is no cloud cover. In addition, the techniques for coastline detection witth SAR images can be automated. In this paper, we present the algorithmic development of an automatic coastline detector for use with SAR imagery. Three main algorithms comprise the automatic coastline detection algorithm, The first algorithm considers the image pre-processing steps that must occur on the original image in order to accentuate the land/water boundary. The second algorithm automatically follows along the accentuated land/water boundary and produces a single-pixel-wide coastline. The third algorithm identifies islands and marks them. This report describes in detail the development of these three algorithms. Examples of imagery are used throughout the paper to illustrate the various steps in algorithms. Actual code is included in appendices. The algorithms presented are preliminary versions that can be applied to automatic coastline detection in SAR imagery. There are many variations and additions to the algorithms that can be made to improve robustness and automation, as required by a particular application.

In this paper, the authors explore the feasibility of using the distributed Bragg reflector, grown on the substrate for a VCSEL (Vertical Cavity Surface Emitting Laser), to provide waveguiding within the substrate. This waveguiding could serve as an interconnection among VCSELs in an array. Before determining the feasibility of waveguide interconnected VCSELs, two analysis methods are presented and evaluated for their applicability to this problem. The implementations in Mathematica of both these methods are included. Results of the analysis show that waveguiding in VCSEL structures is feasible. Some of the many possible uses of waveguide interconnected VCSELs are also briefly discussed. The tools and analysis presented in this report can be used to evaluate such system concepts and to do detailed design calculations.

Passive optical components can be broken down into two main groups: Refractive elements and diffractive elements. With recent advances in manufacturing technologies, diffractive optical elements are becoming increasingly more prevalent in optical systems. It is therefore important to be able to understand and model the behavior of these elements. In this report, we present a thorough analysis of a completely general diffractive optical element (DOE). The main goal of the analysis is to understand the diffraction efficiency and power distribution of the various modes affected by the DOE. This is critical to understanding cross talk and power issues when these elements are used in actual systems. As mentioned, the model is based on a completely general scenario for a DOE. This allows the user to specify the details to model a wide variety of diffractive elements. The analysis is implemented straightforwardly in Mathematica. This report includes the development of the analysis, the Mathematica implementation of the model and several examples using the Mathematical analysis tool. It is intended that this tool be a building block for more specialized analyses.

Radiation testing of photonic components is not new, however component level testing to date has not completely addressed quantities which are important to system behavior. One characteristic that is of particular importance for optical processing systems is the frequency response. In this report, we present the analysis of data from an experiment designed to provide a preliminary understanding of the effects of radiation on the frequency response of acousto-optic devices. The goal of the analysis is to describe possible physical mechanisms responsible for the radiation effects and to discuss the effects on signal processing functionality. The experiment discussed in this report was designed by Sandia National Laboratories and performed by Sandia and Phillips Laboratory personnel at White Sands Missile Range (WSMR). In the experiment, a TeO{sub 2} slow shear wave acousto-optic cell was exposed to radiation from the WSMR linear accelerator. The TeO{sub 2} cell was placed in an experimental configuration which allowed swept frequency diffracted power measurements to be taken during radiation exposure and recovery. A series of exposures was performed. Each exposure consisted of between 1 to 800, 1{mu}sec radiation pulses (yielding exposures of 2.25 kRad(Si) to 913 kRad(Si)), followed by recovery time. At low total and cumulative doses, the bandshape of the frequency response (i.e. diffracted power vs. frequency) remained almost identical during and after radiation. At the higher exposures, however, the amplitude and width of the frequency response changed as the radiation continued, but returned to the original shape slowly after the radiation stopped and recovery proceeded. It is interesting to note that the location of the Bragg degeneracy does not change significantly with radiation. In this report, we discuss these effects from the perspective of anisotropic Bragg diffraction and momentum mismatch, and we discuss the effect on the signal processing functionality.