Publications & Presentations

An ultra-compact shortwave infrared hyperspectral imaging system

Jay Giblin, Rusha Chatterjee, Michael Chase, Michael Ascenzi, Jonathan Rameau, Julia Dupuis, Jacob A. Martin, and Joseph Meola
SPIE Defense + Commercial Sensing, 4-7 April, 2022, Orlando, FL

Physical Sciences Inc. has developed an ultra-compact shortwave infrared (SWIR) staring mode hyperspectral imaging (HSI) sensor with an additional visible full motion video (FMV) capability.

The innovative HSI design implements a programmable micro-electromechanical system entrance slit that breaks the interdependence between vehicle speed, frame rate, and spatial resolution of conventional push-broom systems and enables staring-mode operation without cooperative motion of the host vehicle or aircraft. The FMV and HSI components fit within 1000 cm3, weigh a total of 2.1 lbs., and draw 15 W of power. The sensor mechanical design is compatible with gimbal-based deployment allowing for easy integration into ground vehicles or aircrafts. The FMV is capable of achieving NIRS-6 imagery over a 6°×6° field-of-view (FOV) at a 1500 ft. standoff. The SWIR HSI covers a spectral range of 900-1605 nm with a 15 nm spectral resolution, and interrogates a 5°×5° FOV per 1.6 s with a 2.18 mrad instantaneous FOV (1 m ground sample distance at 1500 ft.). A series of outdoor tests at standoffs up to 300 ft. have been conducted that demonstrate the payload’s capability to acquire HSI information. The payload has direct utility towards diverse remote sensing applications such as vegetation monitoring, geological mapping, surveillance, etc. The data product utility is demonstrated through the spectral identification of materials (e.g. foam and cloth) placed in the sensor’s FOV.

One Dimensional Convolutional Neural Networks for Spectral Analysis

Michael S. Primrose, Jay Giblin, Christian Smith, Martin R. Anguita, Gabriel H. Weedon
SPIE Defense + Commercial Sensing, 4-7 April, 2022, Orlando, FL

A platform for building sensor specific machine learning detection algorithms has been developed to classify spectroscopic data. The algorithms are focused on long wave infrared reflectance (LWIR) and Raman spectroscopies. The classification algorithm is based on a one dimensional (1D) convolutional neural network (CNN) architecture.

Training data is generated using an appropriate signal model that is combined with sensor specific characteristics such as spectral range, spectral resolution, and noise.
Within this paper, the performance of trained CNNs for both LWIR and Raman sensor systems has been evaluated. The evaluation uses both real and synthetic data to benchmark the performance in terms of the discriminant signal. The evaluation data consists of various chemical representations and varied noise levels. The performance of the 1D CNN approach has demonstrated high classification accuracies on data with low discriminant signals. Specifically, the CNNs have demonstrated a classification accuracy >90% for infrared reflectance data down to a wavelength averaged discriminant SNR>1. For Raman systems, we have demonstrated classification accuracies >90% for data with a peak discriminant SNR of approximately 6.

Compressive Sensing Hyperspectral Imager in the LWIR for Chemical Plume Detection

Stephanie M. Craig, Julia R. Dupuis, John P. Dixon, Martín Anguita, David Mansur, S. Chase Buchanan, Eric R. Kehoe, Chris Peterson, Louis Scharf, Michael M. Kirby
SPIE Defense + Commercial Sensing, 4-7 April, 2022, Orlando, FL

An environmentally hardened compressive sensing hyperspectral imager (CS-HSI) operating in the long wave infrared (LWIR) has been developed for low-cost, standoff, wide area early warning of chemical vapor plumes.

The CS-HSI employs a single-pixel architecture achieving an order of magnitude cost reduction relative to conventional HSI systems and a favorable pixel fill factor for standoff chemical plume imaging. A low-cost digital micromirror device modified for use in the LWIR is used to spatially encode the image of the scene; a Fabry-Perot tunable filter in conjunction with a single element mercury cadmium telluride photo-detector is used to spectrally resolve the spatially compressed data. A CS processing module reconstructs the spatially compressed spectral data, where both the measurement and sparsity basis functions are tailored to the CS-HSI hardware and chemical plume imaging. An automated target recognition algorithm is applied to the reconstructed hyperspectral data employing a variant of the adaptive cosine estimator for detection of chemical plumes in cluttered and dynamic backgrounds. The approach also offers the capability to generate detection products in compressed space with no CS reconstruction. This detection in transform space can be performed with a computationally lighter minimum variance distortionless response algorithm, resulting in a bandwidth advantage that supports efficient search and confirm modes of operation.

A solid-state deep ultraviolet spatial heterodyne Raman system for standoff chemical detection

Rusha Chatterjee, Katharine Lunny, Michael Hilton and Jay Giblin
SPIE Defense + Commercial Sensing, 3-7 April 2022, Orlando, FL

There is an on-going need for sensor technologies capable of providing non-contact chemical detection and identification in the defense community. Here, we present the development of a standoff deep ultraviolet (DUV) Raman sensor for the detection of explosive residues. The sensor is based on a solid-state DUV excitation source coupled with a Spatial Heterodyne Spectrometer (SHS) receiver.

The sensor is designed to detect Raman signals from a 4 cm2 area surface at a 1 m standoff. Detection and identification is achieved by correlating measured Raman signatures with high fidelity library spectra. The DUV excitation enables operation in a solar blind spectral region, leverages v4 cross section scaling and resonance enhancement of Raman signatures, and minimizes the impact of sample fluorescence. The SHS receiver provides a ~1000× higher etendue than conventional slit-based spectrometers in a compact and rugged form factor, allowing for high performance field use. This work describes the system design and architecture of the Raman sensor prototype. Developmental standoff Raman measurements with the sensor using bulk liquid and solid samples are presented. Traceability to detection at the μg/cm2 scale is demonstrated and future improvements to increase system standoff are discussed.

Multipath Extinction Detector for Chemical Sensing

Elizabeth C. Schundler, David J. Mansur, Michael Hilton, John Dixon, Stephanie Craig, and Julia R. Dupuis
SPIE Defense + Commercial Sensing, 4-7 April 2022, Orlando, FL

A novel multi-path extinction detector (M-PED) is being developed for point detection, identification and quantification of vapor phase chemicals. M-PED functions by pairing a broadband long-wave infrared (LWIR) quantum cascade laser with a novel sample cell, designed to simultaneously measure chemical absorption at multiple pathlengths and wavelengths.

The pathlength samples are angularly separated in one dimension, such that a diffraction grating can be used to measure wavelength data in the orthogonal dimension using a compact, low-cost microbolometer array. The resulting data matrix is fit to Beer’s Law in two dimensions to accurately quantify chemical concentration while rejecting common mode noise (e.g. laser amplitude noise). The design, characterization and a capability demonstration of the advanced prototype sensor are presented.