This PDF file contains the front matter associated with SPIE Proceedings Volume 8870, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

The Aalto-1 is a 3U-cubesat project coordinated by Aalto University. The satellite, Aalto-1, will be mainly built by students as project assignments and thesis works. The Aalto-1 is planned to launch on 2014. VTT Technical Research Centre of Finland is developing the main Earth observation payload, a miniaturized spectral imager unit, for the satellite. The spectral imager unit contains a spectral imager, a visible RGB-camera and control electronics of the cameras. Detailed design of the spectral imager unit has been completed and assembly of the spectral imager unit will be done in the autumn 2013. The spectral imager is based on a tunable Fabry-Perot interferometer (FPI) accompanied by an RGB CMOS image sensor. The FPI consists of two highly reflective surfaces separated by a tunable air gap and it is based on a piezo-actuated structure. The piezo-actuated FPI uses three piezo-actuators and is controlled in a closed capacitive feedback loop. The spectral resolution of the imager will be 8-15 nm at full width at half maximum and it will operate in the wavelength range 500-900 nm. Imaging resolution of the spectral imager is 1024x1024 pixels and the focal length of the optics is 32 mm and F-number is 3.4. Mass of the spectral imager unit is approximately 600 grams, and dimensions are 97 mm x 97 mm x 48 mm.

A second-generation long-wave hyperspectral imager based on micro-electro-mechanical systems (MEMS) technology is in development. Spectral and spatial encoding using a MEMS digital micro-mirror device enables fast, multiplexed data acquisition with arbitrary spectral response functions. The imager may be programmed to acquire spectrally selective contrast imagery, replacing more time-consuming hyperspectral data collection. A single-element detector collects encoded data and embedded real-time hardware generates imagery. An internal scanning mechanism enables rapid retrieval of full hyperspectral imagery. The resulting rugged, low-cost sensor will provide chemically specific imagery for applications in gaseous and surface contaminant detection, surveillance, remote sensing, and process control.

Observing System Simulation Experiments (OSSEs) are an important tool for evaluating the potential impact of proposed new observing systems, as well as for evaluating trade-offs in observing system design, and in developing and assessing improved methodology for assimilating new observations. Detailed OSSEs have been conducted at NASA/ GSFC and NOAA/AOML in collaboration with Simpson Weather Associates and operational data assimilation centers over the last three decades. These OSSEs determined correctly the quantitative potential for several proposed satellite observing systems to improve weather analysis and prediction prior to their launch, evaluated trade-offs in orbits, coverage and accuracy for space-based wind lidars, and were used in the development of the methodology that led to the first beneficial impacts of satellite surface winds on numerical weather prediction. In this paper, we summarize early applications of global OSSEs to hurricane track forecasting and new experiments, using both global and regional models, that are aimed at both track and intensity forecasting.

By the end of 2013 NASA and EUMETSAT will have accumulated more than 11 years of AIRS, 6 years of IASI and one year of CrIS data. All three instruments were nominally specified to support the NWC for short term weather forecasting with a five year lifetime, but continue to exceed the accuracy requirement needed for weather forecasting alone. This allows use of their data for a much broader range of applications, including the calibration of broad-band instruments in space and climate research. We illustrate calibration aspects with examples from AIRS, IASI and CrIS using spatially uniform clear conditions, simultaneous nadir overpasses and random nadir samples. The differences between AIRS, IASI and CrIS for the purpose of weather forecasting are small and we expect that the excellent forecast impact demonstrated by the combination of AIRS and IASI will be continued by the combination of CrIS and IASI. Clear data are useful for calibration, but contain no climate signal. The analysis of random nadir samples from AIRS and CrIS identifies larger biases for observation of extreme conditions, represented by 1% and 99%tile data than for non-extreme observations. This is relevant for climate analysis. Resolution of these differences require further work, since they can complicate the continuation of trends established by AIRS with CrIS data, at least for extrema. The unequaled stability of the AIRS data allows us to evaluate trends using random nadir sampled data. We see an increasing frequency in severe storms over land, a decreasing frequency over ocean. The 11 years of AIRS data are too short to tell if these trends are significant from a climate change viewpoint, or if they are parts of multi-decadal oscillations.

The high cost of imaging and sounding from space warrants exploration of new methods for obtaining the required information, including changing the spectral band sets, employing new technologies and merging instruments. In some cases we must consider relaxation of the current capability. In others, we expect higher performance. In general our goal is to meet the VIIRS and CrIS requirements while providing the enhanced next generation capabilities: 1) Hyperspectral Imaging in the Vis/NIR bands, 2) High Spatial Resolution Sounding in the Infrared bands. The former will improve the accuracy of ocean color products, aerosols and water vapor, surface vegetation and geology. The latter will enable the high-impact achieved by the current suite of hyperspectral infrared sounders to be achieved by the next generation high resolution forecast models. We examine the spectral, spatial and radiometric requirements for a next generation system and technologies that can be applied from the available inventory within government and industry. A two-band grating spectrometer instrument called the Moderate-resolution Infrared Imaging Sounder (MIRIS) is conceived that, when used with the planned NASA PACE Ocean Color Instrument (OCI) will meet the vast majority of CrIS and VIIRS requirements in the all bands and provide the next generation capabilities desired. MIRIS resource requirements are modest and the Technology Readiness Level is high leading to the expectation that the cost and risk of MIRIS will be reasonable.

A goal of the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission is to observe highaccuracy, long-term climate change trends over decadal time scales. The key to such a goal is to improving the accuracy of SI traceable absolute calibration across infrared and reflected solar wavelengths allowing climate change to be separated from the limit of natural variability. The advances required to reach on-orbit absolute accuracy to allow climate change observations to survive data gaps exist at NIST in the laboratory, but still need demonstration that the advances can move successfully from to NASA and/or instrument vendor capabilities for spaceborne instruments. The current work describes the radiometric calibration error budget for the Solar, Lunar for Absolute Reflectance Imaging Spectroradiometer (SOLARIS) which is the calibration demonstration system (CDS) for the reflected solar portion of CLARREO. The goal of the CDS is to allow the testing and evaluation of calibration approaches, alternate design and/or implementation approaches and components for the CLARREO mission. SOLARIS also provides a test-bed for detector technologies, non-linearity determination and uncertainties, and application of future technology developments and suggested spacecraft instrument design modifications. The resulting SI-traceable error budget for reflectance retrieval using solar irradiance as a reference and methods for laboratory-based, absolute calibration suitable for climatequality data collections is given. Key components in the error budget are geometry differences between the solar and earth views, knowledge of attenuator behavior when viewing the sun, and sensor behavior such as detector linearity and noise behavior. Methods for demonstrating this error budget are also presented.

This paper looks at a data set, called the SHARE 2010 collect, that has been designed to analyze the various impacts of illumination change on materials. Similar fabric materials were placed on different backgrounds where spectral signatures were analyzed to determined impacts of background adjacency. Hyperspectral, multispectral, and LiDAR modalities were used to image the panels in the above mentioned scenarios. Applications such as material detection with results are used to assess difficulties with finding such panels. The incorporation of point LiDAR data sets and physical models will aid in approximating the correct per-pixel signature to be used in the above mentioned detection scheme. This technique can help mitigate issues related to varying illumination across a scene. All of the processing (i.e., LiDAR, MODTRAN, HSI and detection) is performed in a new GUI tool which runs in the ENVI software.

Though many materials behave approximately as greybodies across the long-wave infrared (LWIR) waveband, certain important infrared (IR) scene modeling materials such as brick and galvanized steel exhibit more complex optical properties¹. Accurately describing how non-greybody materials interact relies critically on the accurate incorporation of the emissive and reflective properties of the in-scene materials. Typically, measured values are obtained and used. When measured using a non-imaging spectrometer, a given material’s spectral emissivity requires more than one collection episode, as both the sample under test and a standard must be measured separately. In the interval between episodes changes in environment degrade emissivity measurement accuracy. While repeating and averaging measurements of the standard and sample helps mitigate such effects, a simultaneous measurement of both can ensure identical environmental conditions during the measurement process, thus reducing inaccuracies and delivering a temporally accurate determination of background or ‘down-welling’ radiation. We report on a method for minimizing temporal inaccuracies in sample emissivity measurements. Using a LWIR hyperspectral imager, a Telops Hyper-Cam², an approach permitting hundreds of simultaneous, calibrated spectral radiance measurements of the sample under test as well as a diffuse gold standard is described. In addition, we describe the data reduction technique to exploit these measurements. Following development of the reported method, spectral reflectance data from 10 samples of various materials of interest were collected. These data are presented along with comments on how such data will enhance the fidelity of computer models of IR scenes.

Spectral reflectance estimation of an object via low-dimensional snapshot requires both image acquisition and a post numerical estimation analysis. In this study, we set up a system incorporating a homemade cluster of LEDs with spectral modulation for scene illumination, and a multi-channel CCD to acquire multichannel images by means of fully digital process. Principal component analysis (PCA) and pseudo inverse transformation were used to reconstruct the spectral reflectance in a constrained training set, such as Munsell and Macbeth Color Checker. The average reflectance spectral RMS error from 34 patches of a standard color checker were 0.234. The purpose is to investigate the use of system in conjunction with the imaging analysis for industry or medical inspection in a fast and acceptable accuracy, where the approach was preliminary validated.

Hyperspectral remote sensing is capable of providing large numbers of spectral bands. The vast amount of data volume presents challenging problems for information processing, such as heavy computational burden. In this paper, the impact of dimension reduction on hyperspectral data clustering is investigated from two viewpoints: 1) computational complexity; and 2) clustering performance. Clustering is one of the most useful tasks in data mining process. So, investigating the impact of dimension reduction on hyperspectral data clustering is justifiable. The proposed approach is based on thresholding the band correlation matrix and selecting the least correlated bands. Selected bands are then used to cluster the hyperspectral image. Experimental results on a real-world hyperspectral remote sensing data proved that the proposed approach will decrease computational complexity and lead to better clustering results. For evaluating the clustering performance, the Calinski-Harabasz, Davies-Bouldin and Krzanowski-Lai indices are used. These indices evaluate the clustering results using quantities and features inherent in the dataset. In other words, they do not need any external information.

The Multi-Slit Optimized Spectrometer (MOS) is a NASA funded Instrument Incubator Program (IIP) to advance an innovative dispersive spectrometer concept in support of the GEO-CAPE ocean science mission. As part of the instruments design and testing, we constructed a `behavioral model' of the instrument's optical engine which allows an end-to-end simulation from input radiances to nal product maps. Here we describe the model used for a rapid, but realistic, simulation of the MOS optical engine, and give illustrative examples of quantitatively tracking errors in the imaging chain from input radiances to bounds on nal product errors.

The facility Optical Information Systems (OS) at the Robotics and Mechatronics Center of the German Aerospace Center (DLR) has more than 30 years of experience with high-resolution imaging technology. This paper shows the scientific results of the institute of leading edge instruments and focal plane designs for EnMAP VIS/NIR spectrograph. EnMAP (Environmental Mapping and Analysis Program) is one of the selected proposals for the national German Space Program. The EnMAP project includes the technological design of the hyper spectral space borne instrument and the algorithms development of the classification. The EnMAP project is a joint response of German Earth observation research institutions, value-added resellers and the German space industry like Kayser-Threde GmbH (KT) and others to the increasing demand on information about the status of our environment. The Geo Forschungs Zentrum (GFZ) Potsdam is the Principal Investigator of EnMAP. DLR OS and KT were driving the technology of new detectors and the FPA design for this project, new manufacturing accuracy and on-chip processing capability in order to keep pace with the ambitious scientific and user requirements. In combination with the engineering research, the current generations of space borne sensor systems are focusing on VIS/NIR high spectral resolution to meet the requirements on earth and planetary observation systems. The combination of large swath and high spectral resolution with intelligent synchronization control, fast-readout ADC chains and new focal-plane concepts open the door to new remote-sensing and smart deep space instruments. The paper gives an overview over the detector verification program at DLR on FPA level, new control possibilities for sCMOS detectors in global shutter mode and key parameters like PRNU, DSNU, MTF, SNR, Linearity, Spectral Response, Quantum Efficiency, Flatness and Radiation Tolerance will be discussed in detail.

The main challenges of fabricating diffraction gratings for use in earth monitoring spectrometers are given by the requirements for low stray light, high diffraction efficiency and a low polarization sensitivity. Furthermore the use in space also requires a high environmental stability of these gratings. We found that holography in combination with ion beam plasma etching provides a way to obtain monolithic, robust fused silica gratings which are able to meet the above mentioned requirements for space applications. Holography accompanied by plasma etching allows the fabrication of a wide range of different grating profiles to optimize the efficiency including the polarization behavior according to a wealth of applications. Typical profile shapes feasible are blazed gratings, sinusoidal profiles and binary profiles and this allows to tailor the efficiency and polarization requirements exactly to the spectral range of the special application. Holographic gratings can be fabricated on plane and also on curved substrates as core components of imaging spectrometers. In this paper we present our grating fabrication flow for the example of plane blazed gratings and we relate the efficiency and stray light measurement results to certain steps of the process. The holographic setup was optimized to minimize stray light and ghosting recorded by the photoresist during the exposure. Low wave front deviations require the use of highly accurate grating substrates and high precision optics in the holographic exposure.

The NASA ESTO funded Multi-slit Optimized Spectrometer (MOS) Instrument Incubator Program will advance a spatial multiplexing spectrometer for coastal ocean remote sensing from lab demonstration to flight like environment testing. Vibration testing to meet the GEVS requirements for a geostationary orbit launch will be performed. The multiple slit design reduces the required telescope aperture leading to mass and volume reductions over conventional spectrometers when applied to the GEO-CAPE oceans mission. The MOS program is entering year 3 of the 3-year program where assembly and test activities will demonstrate the performance of the MOS concept. This paper discusses the instrument design, fabrication and assembly. It outlines the test plan to realize a technology readiness level of 6. Testing focuses on characterizing radiometric impacts of the multiple slit images multiplexed onto a common focal plane, and assesses the resulting uncertainties imparted to the ocean color data products. The MOS instrument implementation for GEO-CAPE provides system benefits that can lead to cost savings and risk reduction while meeting the science objectives of understanding the dynamic coastal ocean environment.

The CHROMA (Configurable Hyperspectral Readout for Multiple Applications) is an advanced Focal Plane Array (FPA) designed for visible-infrared imaging spectroscopy. Using Teledyne’s latest substrateremoved HgCdTe detector, the CHROMA FPA has very low dark current, low readout noise and high, stable quantum efficiency from the deep blue (390nm) to the cutoff wavelength. CHROMA has a pixel pitch of 30 microns and is available in array formats ranging from 320×480 to 1600×480 pixels. Users generally disperse spectra over the 480 pixel-length columns and image spatially over the n×160 pixellength rows, where n=2, 4, 8, 10. The CHROMA Readout Integrated Circuit (ROIC) has Correlated Double Sampling (CDS) in pixel and generates its own internal bias signals and clocks. This paper presents the measured performance of the CHROMA FPA with 2.5 micron cutoff wavelength including the characterization of noise versus pixel gain, power dissipation and quantum efficiency.

The Hyperspectral Imager for the Coastal Ocean (HICO) is the first spaceborne imaging spectrometer designed to sample the coastal ocean. HICO samples selected coastal regions at 92 m ground sample distance with full spectral coverage (88 channels covering 400 to 900 nm) and a high signal-to-noise ratio to resolve the complexity of the coastal ocean. HICO has been operating on the International Space Station since October 2009 and collected over 8000 scenes for more than 50 users. We have been using HICO data to study major rivers and estuaries in the US and Asia. Our results show the advantages of HICO’s additional spectral channels and higher spatial resolution for studying these complex coastal waters. We use these data to suggest requirements for spatial and spectral sampling for future ocean color sensors.

The seafloor, with its diverse and dynamic benthic habitats varying on meter to centimeter scales, is difficult to accurately monitor with traditional techniques. The technology used to build imaging spectrometers has rapidly advanced in recent years with the advent of smaller sensors and better signal-to-noise capabilities that has facilitated their use in mapping fine-scale benthic features. Here, the use of such sensors for hyperspectral remote sensing of the seafloor from both airborne and underwater platforms is discussed. Benthic constituents provide a so-called optical fingerprint with spectral properties that are often too subtle to be discerned with simple color photographs or multichannel spectrometers. Applications include the recent field validation of the airborne Portable Remote Imaging SpectroMeter (PRISM), a new imaging sensor package optimized for coastal ocean processes in Elkorn Slough California. In these turbid sediment-laden waters, only subtle spectral differences differentiate seafloor with sediment from that with eelgrass. The ultimate goal is to provide robust radiometric approaches that accurately consider light attenuation by the water column and are able to be applied to diverse habitats without considerable foreknowledge.

Coral reefs are one of the most diverse and threatened ecosystems in the world. Corals worldwide are at risk, and in many instances, dying due to factors that affect their environment resulting in deteriorating environmental conditions. Because corals respond quickly to the quality of the environment that surrounds them, corals have been identified as bioindicators of water quality and marine environmental health. The hyperspectral imaging system is proposed as a noninvasive tool to monitor different species of corals as well as coral state over time. This in turn can be used as a quick and non-invasive method to monitor environmental health that can later be extended to climate conditions. In this project, a laboratory-based hyperspectral imaging system is used to collect spectral and spatial information of corals. In the work presented here, MATLAB and ENVI software tools are used to view and process spatial information and coral spectral signatures to identify differences among the coral data. The results support the hypothesis that hyperspectral properties of corals vary among different coral species, and coral state over time, and hyperspectral imaging can be a used as a tool to document changes in coral species and state.

Vegetation fluorescence when measured from space contributes only a tiny fraction of the signal coming on top of the reflected radiance by the Earth surface and the atmosphere. As a consequence, imaging spectrometers have to provide sufficient throughput and radiometric accuracy to enable accurate global monitoring of the daily to seasonal variations of the Earth's vegetation breath, which is particularly challenging if ground resolutions of a few hundred meters are targeted. Since fluorescence retrieval algorithms have to make corrections for atmospheric effects, it is necessary to provide sufficient spectral resolution, so that signal alterations due to the main parameters such as surface pressure, atmospheric temperature profile, vertical distribution of aerosols concentration, and water vapour content can be accurately modelled. ESA’s Earth Explorer 8 candidate mission FLEX carries a Fluorescence Imaging Spectrometer (FLORIS), which has been designed and optimised to enable such measurement. The spectrometer will measure in a spectral range between 500 and 780 nm and provide high spectral resolution of 0.3 nm in particular at the Oxygen-A and -B bands. It will also cover the photochemical reflection features between 500 and 600 nm, the Chlorophyll absorption region between 600 and 677 nm, and the red-edge in the region of 697 to 755 nm. FLEX will fly in formation with Sentinel-3 in order to further enhance the spectral coverage from measurements made by the Sentinel-3 instruments OLCI and SLSTR, particularly for cloud screening and proper characterization of the atmospheric status.

A comparative study of three imaging technologies has been conducted to nondestructively assess the thickness and strength of oyster shells grown in various environmental conditions. Oyster shell thickness and strength are expected to be dependent on the harshness of the oyster's environment as well as other factors. Oysters have been grown in environments with and without predators, and within and out of tidal zones. Hyperspectral imaging has been used to detect possible differences in hyperspectral properties among oyster shells from each of the four environments. Thermal Imaging has been utilized to identify hot spots in the shells based on the principles of heat capacitance, indicating density or thickness of the shells. Finally, a visible-range digital photographic camera has been used to obtain digital images. The three technologies are compared to evaluate the effectiveness of each technology in identifying oyster shell thickness and strength. Although oyster shell thickness and strength are related, they may not be exactly correlated. The local thickness of the oyster shells have been measured with a micro caliper, and shells broken with a crush tester to establish a baseline and ground truth for average shell thickness and shell strength, respectively. The preliminary results from the three methods demonstrate that thermal imaging correlates the best with the invasive strength test results and weight measurements. Using hyperspectral data and principal component analysis, classification of the four oyster shell groups were achieved. Visible-range images mainly provided size, shape, color and texture information.

For hyperspectral imaging system design and parameter trade-o research, an analytical model to simulate the remote sensing system has been developed and is in progress to be made available to the community. The analytical model includes scene, sensor and target characteristics, and also atmospheric features, background spectral statistics, sensor speci cations and target spectral statistics. The model is being implemented as a web-based application through an RIT-hosted website. Predicting system performance has been veri ed by real world data collected during the RIT SHARE 2012 collection and the data shows consistency with the simulated results on calibration tarps and grass. Also, subpixel target spectral statistics are predicted by this model. Some parameter trade-o examples are given and analyzed to explain the utility of this model.

Various phenomena occur in geographic regions that cause pixels of a scene to contain spectrally mixed pixels. The mixtures may be linear or nonlinear. It could simply be that the pixel size of a sensor is too large so many pixels contain patches of different materials within them (linear), or there could be microscopic mixtures and multiple scattering occurring within pixels (non-linear). Often enough, scenes may contain cases of both linear and non-linear mixing on a pixel-by-pixel basis. Furthermore, appropriate endmembers in a scene are not always easy to determine. A reference spectral library of materials may or may not be available, yet, even if a library is available, using it directly for spectral unmixing may not always be fruitful. This study investigates a generalized kernel-based method for spectral unmixing that attempts to determine if each pixel in a scene is linear or non-linear, and adapts to compute a mixture model at each pixel accordingly. The effort also investigates a kernel-based support vector method for determining spectral endmembers in a scene. Two scenes of hyperspectral imagery calibrated to reflectance are used to validate the methods. We test the approaches using a HyMAP scene collected over the Waimanalo Bay region in Oahu, Hawaii, as well as an AVIRIS scene collected over the oil spill region in the Gulf of Mexico during the Deepwater Horizon oil incident.

This work presents a radiometric model of a spatial heterodyne spectrometer (SHS) and a corresponding interferogram-processing algorithm for the calculation of calibrated spectral radiance measurements. The SHS relies on Fourier Transform Spectroscopy (FTS) principles, and shares design similarities with the Michelson Interferometer. The advantages of the SHS design, including the lack of moving parts, high throughput, and instantaneous spectral measurements, make it suitable as a eld-deployable instrument. Operating in the long-wave infrared (LWIR), the imaging SHS design example included provides the capability of performing chemical detection based on re ectance and emissivity properties of surfaces of organic compounds. This LWIR SHS model outputs realistic, interferometric data and serves as a tool to nd optimal SHS design parameters for desired performance requirements and system application. It also assists in the data analysis and system characterization. The interferogram-processing algorithm performs at- elding and phase corrections as well as apodization before recovering the measured spectral radiance from the recorded interferogram via the Inverse Fourier Transform (IFT). The model and processing algorithm demonstrate results comparable to those in the literature with a noise-equivalent change in temperature of 0.35K. Additional experiments show the algorithm's real-time processing capability, indicating the LWIR SHS system presented is feasible.

In the hyperspectral theory, data reduction techniques play an important role in the classification processing as hyperspectral imagery contains an immense amount of data posing many challenging problems such as data storage, computational efficiency, and the curse of dimensionality. Hyperspectral band selection technique is a well-known dimensionality reduction approach which retains the physical meaning of the data. It selects a set of bands from the input hyperspectral dataset which comprises the information needed for subsequent hyperspectral image spectroscopy. The majority of the existing state-of-the-art dimensionality reduction methods set criteria to the spectral information which is derived by the whole wavelength in order to define the optimum spectral subspace. These criteria are not associated with the particular classification task but with the data statistics, such as correlation and entropy values. However, each spectral signature of a particular material has spectral characteristics which contribute to distinguish it from other spectral signatures at specific sequential wavelengths. This paper focuses on investigating the effects of band selection on the classification by exploiting the information of sequential bands. More precisely, it is explored 1) whether classification can be optimized when a different set of initial bands is selected per category; 2) whether there is an optimum subset of sequential bands which lead to more accurate classification results. Experiments comprise application of a well-known classification method, the support vector machine (SVM), on real hyperspectral dataset using all the possible subsets of p sequential bands, where p is equal to the dimensionality of the signal subspace. Evaluation of the classification accuracy leads to remarkable conclusions.