This PDF file contains the front matter associated with SPIE Proceedings Volume 11504, including the Title Page, Copyright information, and Table of Contents.

The Earth Surface Mineral Dust Source Investigation (EMIT) instrument is a high fidelity imaging spectrometer developed to characterize surface mineralogy of the Earth's dust source regions over the spectral range of 380- 2500 nm and spectral sampling of 7.4 nm. EMIT will close the current knowledge gap in dust source mineral composition by collecting over 1 billion high signal-to-noise ratio spectra in this region of our planet. These new measurements will be used in conjunction with state-of-the-art Earth System Models to understand and reduce the uncertainty in the radiative forcing effect of mineral dust aerosols. EMIT will be deployed on the International Space Station that has an orbit that is well suited for measuring the arid land regions of the Earth. The optical design utilizes a Dyson spectrometer to reduce volume and mass for a fast (F/1.8) and wide swath (1240 samples) optical system. An overview of the EMIT optical design, development, and current status are discussed.

The Ultra-Compact Imaging Spectrometer Moon (UCIS-Moon) instrument is an imaging spectrometer designed for integration with a lander or rover for lunar surface science missions. Operating over a 600-3600 nm spectral range with 10 nm sampling and 1.15 mrad IFOV, UCIS-Moon is capable of detecting spectral absorptions from common lunar minerals, OH species, molecular H2O, water ice, organics, and placing mineral identifications within an established geologic context at the cm to m scale. We present an instrument design capable of surviving the harsh lunar environment in the daytime with temperatures as high as 370 K, while providing high-quality spectral data.

We report on the design performance of a compact imaging spectrometer suitable for planetary missions with U-Class spacecraft. It is a fast (F/1.9) and wide field instrument covering the range 600-3600 nm with 10 nm sampling and a 1000 pixel by 18 μm wide slit. The spectrometer is designed for use with a digital readout focal plane array that further aids miniaturization.

The Snow and Water Imaging Spectrometer (SWIS) is a science-grade imaging spectrometer and telescope system suitable for CubeSat applications, spanning a 350-1700 nm spectral range with 5.7 nm sampling, a 10 degree field of view and 0.3 mrad spatial resolution. The system operates at F/1.8, providing high throughput for low-reflectivity water surfaces, while avoiding saturation over bright snow or clouds. The SWIS design utilizes heritage from previously demonstrated instruments on airborne platforms, while advancing the state of the art in compact sensors of this kind in terms of size and spectral coverage. Through frequent repeat observations from space at a moderate spatial resolution, SWIS can address key science questions concerning aquatic and terrestrial ecosystem changes, cryosphere warming and melt behavior, cloud and atmospheric science, and potential impacts of climate change and human activities on the environment. We review the optical design and innovations and key technologies developed for this instrument, as well as its measured optical performance. We discuss the radiometric calibration characterization, including detector linearity, flat field correction, and SNR. Finally, we discuss stray light modeling and the development of a focused ghost removal algorithm, which is tested and supported by laboratory results.

The degree of redshift in a photoluminescence spectrum can be applied for position sensing. Because a whole spectrum is not required, replacing a spectrometer with a combination of a dichroic mirror and two silicon photomultipliers (SiPMs) greatly improves its sensitivity. In experiment, a 1 mm-diameter scintillation fiber, a 3.2 mm-thick dichroic mirror and two 6 mm x 6 mm SiPMs were assembled. A 100 ns light pulse from a laser emitting at 377 nm was attenuated by neutral-density filters. When a single spot on the fiber was periodically excited, the output waveforms of the two SiPMs fluctuated. This was due to the statistical variation in the timing of the 377 nm photons entering the SiPMs. By correlating the pulse height of the averaged waveform and the distance between the excitation spot and the tip of the fiber, a calibration curve for position sensing over the distance of 60 cm was obtained. The geometrical efficiency for coupling the light reflected by the dichroic mirror to a SiPM was 0.919 and one for the transmitted light was 0.719. The loss came from the light spreading in the space around the dichroic mirror and the reflection on the mirror surfaces. This geometrical efficiency will be improved by using focusing lenses. Embedding the dichroic mirror and the tip of the fiber in a transparent medium will eliminate the reflection loss. This technology might be applicable for radiation detection.

We propose a technique for reconstruction from incomplete compressive measurements. Our approach combines compressive sensing and matrix completion using the consensus equilibrium framework. Consensus equilibrium breaks the reconstruction problem into subproblems to solve for the high-dimensional tensor. This framework allows us to apply two constraints on the statistical inversion problem. First, matrix completion enforces a low rank constraint on the compressed data. Second, the compressed tensor should be consistent with the uncompressed tensor when it is projected onto the low-dimensional subspace. We validate our method on the Indian Pines hyperspectral dataset with varying amounts of missing data. This work opens up new possibilities for data reduction, compression, and reconstruction.

Remote imaging spectroscopy's role in Earth science will grow in the coming decade as a series of globe-spanning spectroscopy missions launch from NASA, ESA, and other agencies. The nature of remote imaging spectroscopy will change, advancing from short regional studies to address global multi-year questions. The diversity of data will also grow with exposure to a wider range of biomes and atmospheric conditions. To execute these new investigations we must reconcile diverse observing conditions to derive consistent global maps. To this end, rigorous uncertainty quantification and propagation enables an optimal synthesis of data accounting for observing conditions and data quality. Understanding data uncertainties is also important for principled hypothesis testing, information content assessment, and informed decision making by end users. We survey prior efforts in uncertainty quantification for imaging spectroscopy, and describe methods for validating the accuracy of uncertainty predictions. We conclude with a discussion of remaining challenges and promising avenues for future research.

A novel automated channel-selection method based on the gas sensitivity and weighting function characteristics has been applied on simulated ultra-spectral thermal infrared data for CO profile retrieval in our previous work. The method consists of two steps: 1) channels with abundant gas information and insensitivity to other gases are selected as the initial channel group, 2) the optimal channel group is then obtained by optimizing the distribution of the weighting function. The retrieval results show that the method can reduce the redundancy of channels and improve the retrieval accuracy and efficiency of CO profiles. In this paper, the proposed method is assessed by applying on the retrieval of O₃ and CH₄ profiles from the ultra-spectral data and then a set of channels are selected for each gas and atmospheric situation. By comparing to the Optimal Sensitivity Profile (OSP) method, which suggests good performance in the literature, it shows that the selected channels by the proposed method in all the sets are less correlated and some channels with special information but relatively low sensitivity are screened. The root mean square errors (RMSEs) of the most retrieved gas profiles by the novel method are smaller than these by the OSP one. The results indicate that the automated channel-selection method can facilitate the retrieval accuracy for different gas profiles from ultra-spectral data and may have application in the ultra-spectral feature selection and data compression.

The application of compressive sensing (CS) techniques for the hyperspectral (HS) imaging is very appealing since the acquisition of HS images is demanding in terms hardware and acquisition time, and since the application of CS framework matches well the HS imaging task, which involves capturing huge amount of typically very redundant data. During the last decade, we developed several CS HS imaging systems, which have demonstrated orders of magnitude reduction of the acquisition time and of storage requirements, improved signal-to-noise ratio, and reduction of the systems’ size and weight. In this paper we demonstrate how these systems can further benefit from employing deep learning (DL) tools for post-processing of the compressively sensed hyperspectral data. We overview some DL techniques that we have developed for improving the HS image reconstruction and target detection.

A common anomaly detection algorithm for hyperspectral imagery is the RX algorithm based on the Mahalanobis distance of each pixel from the expected value of that pixel. This algorithm can be applied either directly on a hyperspectral image or on a dimensionality-reduced hyperspectral image. Recent work on Non-Negative Matrix Factorization (NNMF) provides a fast-iterative algorithm for decomposing a hyperspectral cube and achieving dimensionality reduction. In this paper, we present the RICHARD (Robust Iterative Consensus Anomaly RX Detection) algorithm that generates more than 100 RX tests after data manipulations (such as Principal Component Analysis (PCA) and NNMF) which vary in their specific parameters; we then use a weighted consensus voting process in order to detect anomalies without any prior knowledge. Using the RICHARD algorithm can enhance our options in finding obscure anomalies which do not appear in every algorithm.

Most plastics are typically transparent in the visible spectral range, rendering them challenging to detect using silicon-based vision sensors. In this work a SWIR hyperspectral imaging system is used to collect the SWIR hyperspectral signatures as well as spatial information of a variety of plastics outdoors to test this technology for plastic debris detection and identification in future marine and environmental applications. In this study, hyperspectral imaging data have been collected from plastic samples including CPVC, PVC, LDPE, HDPE, PEEK PETG, PC, PP, PS, and Polyester in a natural environment. The data is acquired using a SWIR hyperspectral imaging system sensitive to 900 - 1700 nm wavelength range. Four spectral indices based on labeled spectral signatures have been identified and used as features to separate plastic materials and for classification of pixels. Semantic segmentation based on plastic materials is achieved in an independent scene with multiple plastic samples using shortest Euclidean distance to labeled feature cluster centers through multi-variate data analysis. The results show the capability of this technology and technique to detect and classify different plastics in natural environments under different light conditions.

Post-fire forest management is been increasingly based on fire damage maps derived from satellite imagery. Though Landsat data have been the most commonly used at medium scale (<100m / pixel), Sentinel 2 satellites provide an opportunity for post-fire damage analysis. MultiSpectral Instrument (MSI) onboard of Sentinel 2 satellites acquires data in red-edge wavelengths, and has higher spatial (10/20m vs. 30m) and temporal (16 vs. 5 days) resolution. Thus, the aim of this study is to check whether Sentinel 2 MSI spectral indexes that include red-edge bands allow a better discrimination between burned and unburned areas than conventional spectral indexes based on red, near infrared and/or short wave infrared. A large forest fire (79.5 km²) occurred in Sierra de Gata (Spain) in August 2015 acted as study area. Official fire perimeter together to Copernicus Emergency Management Service information (ID: EMSR132) provided us a terrain reference. Logistic regression models based on Sentinel 2 MSI spectral indexes (conventional and red-edge based) showed that red-edge spectral indexes outperformed conventional ones in terms of discriminating burned from unburned areas.