KEYWORDS: Image compression, Chemical analysis, Wavelets, Hyperspectral imaging, Image filtering, Data modeling, Digital micromirror devices, Statistical analysis, Sensors, Image resolution, Multiresolution signal processing, Long wavelength infrared, Chemical detection
In this paper we derive two algorithms for estimating concentrations of a known chemical compound from compressed measurements of a hyperspectral image (HSI). It is assumed that each resolved pixel in a scene contains a chemical of known spectral signature, at an unknown concentration. The problem is to estimate the concentration directly from the compressed measurements. Estimated concentrations are either displayed or used as detection scores in a threshold test for presence or absence of chemical. In the first algorithm we use matched filtering and ℓ1 regularization to extract an image of concentrations, directly from compressed data. In the second we model the image of concentrations in a fixed-resolution subspace of the 2D Haar wavelet domain, estimate its parameters in this space, and reconstruct the image of concentrations at a macro-pixel resolution. We evaluate our algorithms by applying them to several long-wave infrared (LWIR) HSI data sets, either synthetically generated or recorded by Physical Sciences Inc. Synthetically-generated data is compressed with a mathematically-defined linear compressor; real HSI data is compressed with PSI’s Digital Micromirror Device (DMD), which is a physical implementation of a mathematically-defined compressor; Fabry-Perot data is raw HSI data recorded by PSI, which is then compressed with a mathematically-defined compressor. We demonstrate for these data sets that estimating concentrations through matched filtering and ℓ1 inversion of compressed measurements yields detection performance that is as good as previously proposed methods that first reconstruct a hyperspectral data cube from compressed data, and then estimate or detect chemical concentrations. The proposed methods save on memory and computation. We demonstrate that detection performance is maintained when resolving concentration maps at a lower resolution, so long as the resolution is not too low.
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 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.
KEYWORDS: Short wave infrared radiation, Vegetation, Sensors, Reflectivity, Digital micromirror devices, Signal to noise ratio, Spectral resolution, Data acquisition, Infrared imaging, Infrared radiation
A high-speed visible/near infrared, shortwave infrared (VNIR/SWIR) hyperspectral imaging (HSI) sensor for airborne, dynamic, spatially-resolved vegetation trait measurements in support of advanced terrestrial modeling is presented. The VNIR/SWIR-HSI sensor employs a digital micromirror device as an agile, programmable entrance slit into VNIR (0.5–1μm) and SWIR (1.2–2.4μm) grating spectrometer channels, each with a two-dimensional focal plane array. The sensor architecture, realized in a 13 lb package, is specifically tailored for deployment on a small rotary wing (hovering) unmanned aircraft system (UAS). The architecture breaks the interdependency between aircraft speed, frame rate, and spatial resolution characteristic of push-broom HSI systems. The approach enables imaging while hovering as well as flexible revisit and/or foveation over a region of interest without requiring cooperation by the UAS. Hyperspectral data cubes are acquired on the second timescale which alleviates the position accuracy requirements on the UAS’s GPS-IMU. The sensor employs a simultaneous and boresighted visible context imager for pan sharpening and orthorectification. The data product is a 384×290 (spatial) ×340 (spectral) format calibrated, orthorectified spectral reflectivity data cube with a 26×20° field of view. The development, characterization, and a series of capability demonstrations of an advanced prototype VNIR/SWIR HSI sensor are presented. Capability demonstrations include ground-based testing as well as flight testing from a commercial rotary wing UAS with remote operation of the HSI sensor via a dedicated ground station.
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