A lattice-based target design is presented for expanding research capabilities in subpixel target detection. The targets generate large numbers of subpixel samples with a priori knowledge of the exact subpixel fractions. This contrasts with traditional targets, where subpixel fractions are either unknown or estimated with significant uncertainty, with limited samples available in historical datasets. The subpixel targets diminish these drawbacks and generate constant subpixel samples invariant to effects of the system (e.g., image distortions, scan pattern) which would typically induce uncertainty. Simulations were performed to assess the accuracy of the proposed method of achieving samples with constant fractions. To validate and demonstrate the functionality of the design, four targets were fabricated with constant subpixel fractions (0.2, 0.4, 0.6, 0.8) and were deployed into a hyperspectral data collection. Spectral unmixing validated the retrieval of samples with constant fractions, and a general target detection scenario was demonstrated using 300–400 samples of each constant fraction. The impacts of a limited number of target samples (e.g.,$n = 5,10$) on receiver operating characteristic (ROC) curves were empirically assessed, with a significant reduction of variability observed when$n > 100$, illustrating the advantages when large sample sizes are available. Design limitations are discussed, along with applications (e.g., algorithm comparison) for the community.

Objects on Earth with 3-dimensional geometries cast shadows onto the underlying background due to the obstruction of direct solar radiation. This phenomenon is considered for a multiclass subpixel target detection scenario, where the mixed pixels consist of materials from varying percentages of an illuminated target and partially shaded background. Hyperspectral data collected from UAS-based instruments and novel subpixel targets were used for the analyses. The novel targets enabled empirical observations of fully illuminated targets on partially shaded backgrounds. To assist in developing inferences on the impacts of detection, model reflectance spectra were generated for mixed pixels with fully illuminated and fully shaded background conditions. The modeling approach was validated with the empirical observations. The results imply the shadow effect is an important system parameter for subpixel target percentages of 20% or less, for the multiclass scenario and particular targets explored.

In the realm of hyperspectral sub-pixel target detection, the Linear Mixing Model (LMM) is an established basis for analysis and modelling. However, its accuracy depends on several key assumptions, most notably that there exists no nonlinear mixing within a scene. The Forecasting and Analysis of Spectroradiometric System Performance (FASSP) model utilizes the LMM to perform system requirement analyses. To quantify the limitations of the LMM when nonlinear effects are present, this paper reviews the results of September 2022 data collect in which the spectra of several sub-pixel target panels were altered by shadowing and reflectance panels and compares it with those from FASSP. Overall, both forms of nonlinear effects reduce target detection performance and the FASSP model tends to overestimate target radiance and target detection performance relative to the empirical results when nonlinear effects are present.

Particulate Matter (PM) 2.5 is a critical factor to measure in the environment. However, accurate PM 2.5 measurement relies on special devices, which leads to limited spatial coverage. This paper focuses on the development of PM 2.5 estimation based on satellite images. We have developed a deep temporal multiple layer perception (MLP) neural network based on various satellite imaging inputs captured from different time periods together with meteorological inputs. In addition, the temporal MLP simultaneously learns both PM 2.5 and PM 10 which can improve the performance of each task. Experiments demonstrate that our method outperforms other machine learning algorithms used in PM 2.5 estimation.

Traditional measurement of the air pollutant particulate matter known as PM2.5 is performed by ground monitoring stations. These measurements are sparse in spatial extent due to the limited number of monitoring stations and their uneven distribution. In this study we explored the use of satellite images together with a convolutional recurrent neural network to predict a category of PM2.5 concentration for continuous spatial samples across a region. Aerosol optical depth (AOD) and Top of Atmosphere (TOA) products from NASA's MODIS instrument were used to provide spatial and temporal data to the network trained to predict the PM2.5 category. Results demonstrate an over 70% classification accuracy for the PM2.5 category.

Surface concentrations of particulate matter less than 2.5 microns in diameter (PM2.5) are increasingly important as a representative measure of air quality. Studies clearly demonstrate short-term and long-term health impacts due to increases in PM2.5 concentrations. Global in-situ surface observations are routinely made using both expensive regulatory grade monitors and low-cost sensors. However, these measurements do not cover all locations of interest around the globe. Since obtaining adequate ground-based monitor coverage would be cost-prohibitive, there is considerable global interest in instruments aboard earth orbiting satellites and numerical chemical transport models as possible solutions to fill data gaps. Current sources of satellite-derived global estimates and model forecasts were identified and reviewed. Initial evaluations of published accuracies of these data suggest challenges remain to using these sources for accurate, reliable monitoring and forecasting. However, satellite derived PM2.5 estimates were reported to be reliable at longer time intervals, such as one year.

Vegetation biophysical parameter retrieval is an important earth remote sensing system application. In this paper, we studied the potential impact of the addition of new spectral bands in the red edge region in future Landsat satellites on agroecosystem canopy chlorophyll content (CCC) retrieval. The test data were simulated from SPARC `03 field campaign HyMap hyperspectral data. Two retrieval approaches were tested: empirical regression based on vegetation index (VI) and physical model-based look-up-table (LUT) inversion. The results of both approaches showed that a potential new spectral band located between the Landsat-8 Operational Land Imager (OLI) red and NIR bands slightly improved the agroecosystem CCC retrieval accuracy (R2of 0.853 vs. 0.875 for vegetation index approach, 0.500 vs. 0.570 for LUT inversion approach).

We explore an analytic modeling tool developed to predict utility of spectral imaging systems for subpixel target detection and compare the prediction to assessment of spectral images of a comparable scene generated using a physics-based simulation model. We predict performance of a system design using the Forecasting and Analysis of Spectro-radiometric System Performance (FASSP) model. Then we assess the performance and supplement the prediction using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model to test subpixel target detection performance on simulated images. We present the initial results of comparison of a simple open ocean scene with orange lifeboat targets between the FASSP prediction and assessment using DIRSIG images. For the target considered, both the analytic and simulation approaches showed comparable detection.

Monitoring and mapping atmospheric methane is becoming increasingly important due to its role as a greenhouse gas and the fact its atmospheric concentration has been rising in recent years. Many remote sensing techniques exist to detect the presence of methane in the atmosphere including active sensing with lidar systems, passive sensing in the solar reflective spectral region, and passive sensing in the longwave thermal infrared. This study examined the temperature contrast in the longwave infrared of an enhanced concentration of methane in a near surface plume for a variety of concentrations, plume temperatures, and plume thicknesses. Results indicate radiometric temperature absolute differences for a narrow band (50 - 200 nm) centered on the 7.68 μm methane feature ranged from 0.0 to 6.8 K for the conditions studied. Future work will further validate and expand on these results.

The Landsat program plays an important role in providing continuous long-term multispectral moderate resolution observations of the earth’s surface. There is an interest by the community to improve the temporal sampling in future generations of the sensors. One way to achieve higher sampling is to collect imagery with a wider swath. For future instruments using multilayer dielectric filters for band selection, this has the further implication that light may enter the filters on the detectors at a higher angle of incidence, which will shift the center wavelength of the spectral bandpasses. Through simulation of a forest environment this paper explored the impact of this effect on measured spectral radiance and the normalized vegetation difference index. The effect is quantified both for angles of incidence seen in the current Landsat-8 Operational Land Imager as well as for pushbroom designs with up to 13° off-axis imaging. Results indicate the effect will be significant compared to instrument noise and comparable to the limit of current requirements on filter manufacturing tolerances.

Simulated remote sensing imagery is valuable for instrument design studies, analysis algorithm development and validation, as well as analyst training. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) suite of software is an image simulation tool developed by the Digital Imaging and Remote Sensing (DIRS) Laboratory at the Rochester Institute of Technology. DIRSIG has a long history of supporting sensor design. In this paper simulated Landsat-8 Operational Land Imager (OLI) imagery generated with DIRSIG is validated by comparison of several metrics with those obtained from analysis of real Landsat-8 OLI imagery. Comparison metrics include the data covariance matrix eigenstructure, supervised classification accuracies and NDVI product values. The results show that when evaluated by these comparison metrics the simulated imagery captures the key characteristics of images for a variety of scenes.

In order to ensure homogeneity in performance assessment of proposed algorithms for information extraction in the Earth Observation (EO) domain, standardized remotely sensed datasets are particularly useful and welcome. Fully aware of this principle, the IEEE Geoscience and Remote Sensing Society (GRSS) and especially its Image Analysis and Data Fusion Technical Committee (IADF), has been organizing for some years now the Data Fusion Contest (DFC). In the DFC, one specific dataset is made available to the scientific community, which can download it and use it to test its newly developed algorithms. The consistence of the starting dataset across participating groups ensures the significance of assessing and ranking results, to finally proclaim the winner who scored the highest. More recently, the IEEE GRSS has provided one more contribution to the standardization effort by building the Data and Algorithm Standard Evaluation (DASE) website. DASE can distribute to registered users a limited set of possible “standard” open datasets, together with some ground truth info, and automatically assess the processing results provided by the users. In this paper we report on the birth of this initiative and present some recently introduced features.

Reference data (“ground truth”) maps are commonly used to quantitatively assess the performance of imaging spectrometer classification algorithms. However, standard reference data scenes typically are not sufficiently detailed to support assessment of spectral unmixing algorithms. Furthermore, commonly used reference data often lack validation reports that estimate error in the reference data itself, and new reference data are prohibitively expensive to generate using traditional methods. This paper presents a summary of our work, which is focused on introducing new methodologies to efficiently generate and validate abundance map reference data (AMRD), which can then be applied to assess the performance of spectral unmixing on real remotely sensed imagery. AMRD, generated using our methodology, had a validated mean and standard deviation error of 3.0% and 6.3%, respectively, which rivaled the accuracy the best traditional methods. A separate experiment designed to replicate our methodology, using different scenes and imagery, confirmed the relative accuracy of our techniques.

After Malaysian Airlines Flight 370 (MH370) disappeared over the Indian Ocean, claims were made that had the planned Planet Labs small satellite constellation been complete, the wreckage would have been found quickly. The Planet Labs system, when complete, will provide once daily images of every point on Earth at three to five meter spatial resolution. The possibility of using such a system to help in the search for aircraft downed in the ocean was examined, taking into account the probability that the image would be captured by the system and the likelihood that wreckage would be identifiable in such an image. Given the conditions of image capture and some information about the rate at which aircraft wreckage will sink, it was found that the probability of capturing an image of the wreckage was 6.92×10-4if the wreckage sank in 30 minutes, and 1.725×10-6if the wreckage sank in 90 seconds. If the image was captured, human analysts were able to identify the wreckage in simulated imagery with a probability of detection of 0.8 and a probability of false alarm of 0.0. Machine analysis proved less accurate, resulting in PD= 0.73 and PFA= 0.33. Given the low probability that the aircraft wreckage could be imaged using a satellite system, even under the optimistic assumption that the aircraft crashed entirely intact and sank in 30 minutes, it is unlikely that a Planet Labs-like system could have assisted in the search for MH370.

Few studies have evaluated how per-pixel structural configurations could impact spectral response. This has an impact on how we assess especially large area/global ecosystems. In an effort to understand this impact of sub-pixel structural variation on large-footprint imaging spectroscopy, a simulation approach was used, which provides precise knowledge of target geometry and radiometry. We demonstrated the validity of the proposed simulation in terms of one such structural metric of interest, namely leaf area index (LAI). LAI is a key vegetation structural parameter, which has implications for predicting ecosystems' foliar spatial distribution, health, photosynthesis, transpiration, and energy transfer. Simulated LAI measurements were validated with field data obtained from AccuPAR measurements (R2= 0.76) and by comparison to NDVI data obtained from simulated AVIRIS imagery (R2= 0.92−0.65, depending on sampling interval). These data were used to propose an appropriate sampling protocol for LAI data collection, thus providing for efficient data collection, while minimizing variability of individual measurements. These efforts will support preparatory science experiments towards understanding the phenomenology of NASA's next-generation imaging spectrometer, HyspIRI.

The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) mission of the National Aeronautics and Space Administration is scheduled to launch in 2017. This upcoming mission aims to provide data to determine the temporal and spatial changes of ice sheet elevation, sea ice freeboard, and vegetation canopy height. A photon-counting lidar onboard ICESat-2 yields point clouds resulting from surface returns and noise. In support of the ICESat-2 mission, this letter derives an adaptive density-based model that is capable of detecting the ground surface and vegetation canopy in photon-counting laser altimeter data. Based on results from point clouds generated by a first principle simulation and those observed by the Multiple Altimeter Beam Experimental Lidar, the ground and canopy returns can be reliably extracted using the proposed approach. Further study on performance assessment shows that smoother surfaces will result in improved accuracy of ground height estimation. In addition, the proposed detection approach has better performance in environments with lower noise, although the performance evaluation metric F-measure does not vary significantly over a range of noise rates (0.5-5 MHz). This proposed approach is generally applicable for surface and canopy finding from photon-counting laser altimeter data.

This paper reports the initial investigation of the use of cube-satellites (cubesats) for image-based, three-dimensional (3D) reconstruction. Cubesats are emerging as a low-cost, inexpensive, and quickly deployable alternative to their larger satellite predecessors but their functionality is limited by the payload and bus electronics that can fit in the minimal volume. This paper addresses the impact of the spatial resolution limitation of state-of-the-art cubesat imagers on 3D reconstruction, which is assessed with building height and surface normal measurements. For the nadir ground-sampled distance (GSD) range of 0.25 to 2 m, reconstruction results yielded building height estimates that varied by approximately two meters and surface normal estimates with an error ranging from 1-29 degrees depending on the complexity of the surface.

Observations from satellite lidar instruments have provided evidence in the remarkable changes in polar ice sheets on a global scale. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) is scheduled for launch by NASA in 2017 and will monitor the elevation changes of polar ice sheets and vegetation canopy. To validate ICESat-2's approach of photon-counting laser altimetry, measurements obtained from the Multiple Altimeter Beam Experimental Lidar (MABEL) instrument are critical. In support of the ICESat-2 mission, this paper derives an algorithm for the detection of ground and vegetation canopy in photon-counting laser altimeter data. This approach uses a density-based clustering model and modifies the shape of search area. Based on results from MABEL observations, the proposed approach is seen to be robust in detecting ground and vegetation canopy as well as background noise reduction. In addition, this approach can be quickly implemented and adaptive to photon-counting lidar data sets with different point cloud densities.

Optical polarization has shown promising applications in passive remote sensing. However, the combined effects of the scene characteristics, the sensor configurations, and the different processing algorithm implementations on the overall system performance have not been systematically studied. To better understand the effects of various system attributes and help optimize the design and use of polarimetric imaging systems, an analytical model has been developed to predict the system performance. The model propagates the first- and second-order statistics of radiance from a scene model to a sensor model and, finally, to a processing model. Validations with data collected from a division of time polarimeter are presented. Based on the analytical model, we then define a signal-to-noise ratio of the degree of linear polarization and receiver operating characteristic curves as two different system performance metrics to evaluate the polarimetic signatures of different objects, as well as the target detection performance. Several examples are presented to show the potential applications of the analytical model for system analysis.

To advance the science of lidar sensing of complex surfaces as well as in support of the upcoming Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission, this paper establishes a framework that simulates the performance of a spaceborne micropulse photon-counting detector system on a complex surface. A first-principle 3-D Monte Carlo approach is used to investigate returning photon distributions. The photomultiplier tube (PMT) detector simulation takes into account detector dead time and multiple pixels based on the latest ICESat-2 design, as well as photon detection efficiency for probabilistic modeling. To explore system behavior, Fourier synthesis is introduced to create a synthetic surface based on parameters derived from a real data set. A radiometric model using bidirectional reflection distribution functions is also applied in the synthetic scene. Such an approach allows the study of surface elevation retrieval accuracy for landscapes which have different shapes as well as reflectivities. As a case study, returning photon detection on an example snow surface is explored. Based on the simulation results for lidar sensing on synthetic complex surfaces with an elevation range of 10 m across the scene, the spaceborne photon-counting lidar system considered here is seen to have a derived elevation bias of up to 2 cm and a error standard deviation of 10 cm. Further study on multiple-pixel PMT performance for complex surfaces demonstrates that a less rough surface will result in higher accuracy and a surface with a smaller diffuse albedo will result in smaller bias.

Satellite-based and airborne lidar instrumentation has been demonstrated to be a strategic tool in increasing our understanding of Earth's polar cryosphere, specifically that of total mass balance, which is a significant factor in estimating sea level rise due to climate change. The Ice, Cloud and land Elevation Satellite 2 (ICESat-2) will provide accurate estimates of local glacial topographical changes with increased measurement precision and change detection capability. Measurements obtained from the airborne Multiple Altimeter Beam Experimental Lidar (MABEL) instrument, an experimental photon-counting lidar - which uses multiple transmit/receive beams to resolve cross-track slope and elevation changes - are critical to project scientists to develop algorithms that will be used for the upcoming ICESat-2 mission. To aid in this effort, a new noise reduction technique has been developed. The general approach is to 1) divide the received photons into bins, 2) calculate the mode for each bin, and 3) compare each photon elevation to the mode for that bin, retaining only those photons that fall within a set threshold. Because the proposed technique uses statistical analysis to separate the surface elevation photons from the solar background photons, finding the surface return is computationally very light, making it ideal for large data sets, such as those associated with photon-counting lidars.

To advance the science of lidar sensing of complex ice and snow surfaces as well as in support of the upcoming ICESat- 2 mission, this paper establishes a framework to theoretically study a spaceborne micropulselidar returns from snow and ice surfaces. First, the anticipated lidar return characteristics for a sloped non-penetrating surface is studied when measured by a multiple-channel photon-counting detector. Second, an analytical snow reflectance model based on experimental observations is applied in synthetic scene. Based on the simulation results, the spaceborne photon-counting lidar system considered here is seen to have moderate detectability on snow surfaces. In addition, for the penetrating snow model considered here, it is shown that slightly sloped snow terrain with larger snow grain size will result in smaller elevation bias.

Understanding basic scattering mechanisms is critical to characterize objects in polarimetric synthetic aperture radar (PolSAR) imaging. For classifications from PolSAR data and modeling polarimetric signatures, characterization of the basic scattering mechanisms is required with good ground truth information. This paper discusses about a low cost laboratory based PolSAR imaging system to characterize the basic scattering mechanisms. Polarimetric SAR images with single surface reflection is captured that represent single bounce scattering. PolSAR image of a real scene is recorded and the image is overlaid on visible satellite imagery for interpretation. The main motivation behind this work is to collect polarimetric SAR data with ground truth information that represents the basic scattering mechanisms with full control over the scene and the radar.

The popularity of hyperspectral imaging in remote sensing continues to to be adapted in novel ways to overcome challenging imaging problems. This paper reports on some of the latest research efforts exploring the phenomenology of using hyperspectral imaging as an aid in detecting and tracking human pedestrians. An assessment of the likelihood of distinguishing between pedestrians given observable materials and based on signal-to-noise level is presented. Initial results indicate favorable separability can be achieved with signal-to-noise ratios as low as 13 for certain materials. Additionally, an overview of a real-world urban hyperspectral imaging data collection effort is presented.

Lidar sensing has been found to be a useful method of monitoring the dynamics and mass balance of glaciers, ice caps, and ice sheets. However, it is also known that ice surfaces can have complex 3-dimensional structure, which can challenge their accurate retrieval with lidar sensing. In support of future lidar sensing satellite missions, such as the upcoming ICESat-2, a joint research project was recently initiated between the Rochester Institute of Technology (RIT) and the University at Buffalo to study lidar sensing of complex ice surfaces. This effort is supported by NASA's Remote Sensing Theory program and is aimed at advancing the science of lidar sensing. The general approach is to 1) define realistic complex ice surfaces, 2) render lidar image simulations, and 3) compare the resulting data to the known surfaces to gain insight into the phenomenology of lidar sensing of snow and ice. The project will build on existing scientific understanding of light scattering from snow and ice as well as lidar sensor system modeling with a systems engineering end-to-end perspective. Initial results show the simulations capturing realistic scattering of photons in snow volumes and the resulting point clouds measured by a model spaceborne lidar system.

Hyperspectral imaging systems have been shown to enable unresolved object detection through enhanced spectral characteristics of the data. Robust detection performance prediction tools are desirable for many reasons including optimal system design and operation. The research described in this paper explores the general understanding of system factors that limit detection performance. Examples are shown for detectability limits due to target subpixel fill fraction, sensor noise, and scene complexity.

Polarimetric imaging systems have shown promising applications in passive remote sensing. To better understand the effects of various system attributes and help optimize the de sign and use of polarimetric imaging systems, an analytical model is developed to predict the system performance. The model consists of scene, sensor, and processing system components. Some pre-processing procedures such as estimation of the Stokes vector and degree of linear polarization (DoLP) are currently considered in the processing model. Validation with data collected from a division of time polarimeter shows good agreement between model predictions and measurements. It has been shown that the analytical model is able to predict the general polarization behavior and data trends with different scene geometries.

Fully automated land-cover classification from commercial remote sensing satellite imagery has had limited success in part due to their limited spectral bands. New promise of unsupervised analysis has emerged with the recent launch of the eight-band high resolution satellite WorldView-2. In this paper, a fully unsupervised classification algorithm is proposed based on self-organizing maps and watershed segmentation. The results demonstrate that the proposed algorithm performs better in classifying homogeneous regions while achieving better accuracy than k-means.

Fully automated land-cover classification from commercial remote sensing satellite imagery has had limited success in part due to their limited spectral bands. New promise of unsupervised analysis has emerged with the recent launch of the eight-band high resolution satellite WorldView-2. In this paper, a fully unsupervised classification algorithm is proposed based on self-organizing maps and watershed segmentation. The results demonstrate that the proposed algorithm performs better in classifying homogeneous regions while achieving better accuracy than k-means.

Accounting for radiative transfer within the atmosphere is usually necessary to accomplish target detection in airborne/satellite hyperspectral images. In this paper, two methods of accounting for the illumination and atmospheric effects-atmospheric compensation (AC) and forward modeling (FM)-are investigated in their application to target detection. Specifically, several crucial aspects are examined, such as the processing required, the computational complexity, and the flexibility accorded to an imperfect knowledge of acquisition conditions. Real ground-truthed hyperspectral data are employed in order to evaluate the operational applicability of such approaches in a target-detection scenario, as well as their impact on the processing-chain computational complexity. Results indicate that AC is recommended when accurate knowledge of the acquisition conditions is available, and the image has relatively uniform illumination and nonshadowed targets. Conversely, FM is preferred if scene conditions are not well known and when the targets may be subject to varying illumination conditions, including shadowing.

Previous work has shown the ability of waveform LiDAR sensors to accurately describe various land cover types [1] and biomass estimates made in the field [2]. What is lacking, however, is a way to describe the different structural components that are embedded in the digitized backscattered energy from the LiDAR pulse. This study aims to extract structural components from waveform LiDAR data in terms of woody, herbaceous, and bare ground components from data collected over a savanna environment in and around Kruger National Park (KNP), South Africa. These components are comprised of metrics extracted from the waveforms and validated using biomass measurements made in field plots. Different size windows around plot centers, 3 × 3 pixels and 9 × 9 pixels (resulting in 1.5m and 4.5 m footprint, respectively), were used to examine scale effects of larger footprints. It was found that composite waveforms resembling plot sizes (9 × 9) most often are able to describe more than 80% of the woody biomass variability across the entire study site, and individually for two of the three land uses within the area. However, the herbaceous component of the waveform did not correlate well with the field measurements, while the bare ground component was verified visually in a side-by-side comparison with optical imagery.

Polarimetric scene phenomenology yields a remote sensing modality that can be used in tandem with or alternative to panchromatic, multispectral, hyperspectral, or infrared intensity imagery. A synthetic image generator boasting a validated polarimetric modality is extremely valuable when testing optical polarimeter models prior to construction and flight test of real sensor hardware and software. Virtual airborne optical sensors can be modeled and placed above a complex synthetic urban scene to create spectrally varying Stokes vector output imagery. Material reflectances, scene geometries, solar and sensor positions, and varying atmospheric conditions all combine to produce spectropolarimetric sensor-reaching radiance that can be characterized by a degree and angle of polarization image. For this paper, example synthetic images were rendered with the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model and compared with real-life camera imagery of motor vehicles. Polarimetric phenomenology was found to be consistent between modeled and measured imagery.

The phenomenology of genetically engineered plant sentinels as measured by spectral imaging remote sensors is investigated. Plant sentinels have been developed to cease chlorophyll production and rapidly turn white in the presence of a chemical inducer such as hazardous chemicals or environmental pollutants. This work investigates the use of spectral imaging technology to detect the de-greening phenomena remotely. Results demonstrate successful detection of the de-greening phenomena even in the presence of benignly stressed plants.

The utility of an image is an attribute that describes the ability of that image to satisfy performance requirements for a particular application. Building on previous research that defines the assessment of the utility of a spectral image based on the detectability of subpixel targets, this paper examines the prediction of spectral image utility. It first reviews existing methods for predicting spectral image utility and then proposes a new approach in predicting spectral image utility for target detection applications that derive statistical parameters directly from the spectral image. This so-called image-derived approach predicts the likelihood of finding synthetically implanted subpixel targets. Using three airborne hyperspectral images, we benchmark prediction performance by comparing the predicted and assessed utilities, quantifying the accuracy of the prediction, and discussing the time savings associated with prediction. Initial results show utility calculation time savings of up to 40% for a single image, with the potential for greater savings if multiple target types are considered. This method of predicting spectral image utility offers a simple and efficient way of predicting image utility, which can be directly compared with our utility assessments.

The 29 papers in this special issue were originally presented at the 2008 International Geoscience and Remote Sensing Symposium (IGARSS'08), held from July 6 to 11 in Boston, MA.

The utility of an image is an attribute that describes the ability of that image to satisfy performance requirements for a particular application. This paper establishes the context for spectral image utility by first reviewing traditional approaches to assessing panchromatic image utility and then discussing differences for spectral imagery. We define spectral image utility for the subpixel target detection application as the area under the receiver operating curve summarized across a range of target detection scenario parameters. We propose a new approach to assessing the utility of any spectral image for any target type and size and detection algorithm. Using six airborne hyperspectral images, we demonstrate the sensitivity of the assessed image utility to various target detection scenario parameters and show the flexibility of this approach as a tool to answer specific user information requirements. The results of this investigation lead to a better understanding of spectral image information vis-a-vis target detection performance and provide a step toward quantifying the ability of a spectral image to satisfy information exploitation requirements.

This paper presents a geometric extension to the well known Empirical Line Method (ELM) for atmospheric compensation, enabling improved results through the use of 3D scene geometry. Typically, this geometry is assumed to be known, either through direct measurement or analysis of other imagery such as lidar or photogrammetric products. However, when the scene geometry is unknown and only spectral information is available, in certain cases estimates of the surface orientation may be obtained directly from image brightness values. In this paper we derive a means to determine surface orientation estimates from a pair of brightness images, and then use this geometric information to improve the ELM solution.

Methods for generating accurate building models from lidar data have received considerable attention in the recent literature. Many of the proposed techniques examine the data to define dominant planes in the structure, then intersect these planes to determine the location of internal edges and vertices. However, since most airborne lidar datasets are collected from near-nadir orientations, there are usually very few data points that lie on vertical surfaces. As such, it is usually difficult to determine the planes corresponding to exterior walls using data points on these walls. However, if we assume that exterior walls are oriented directly under the outer boundary of the roof structure, we may identify the geometry of these walls by modeling the 2D shape of the building exterior. This paper presents a robust approach for extracting this exterior boundary directly from the lidar data.

Hyperspectral imagery leverages the use of spectral measurements to detect objects of interest in a scene that may not be discernable from their spatial patterns alone. This paper describes a project to make available to the community a set of hyperspectral airborne images on which anyone can run a target detection algorithm to find selected objects of interest in an image. The image and target spectral signatures are publicly available but the pixel location of targets within the image is withheld to allow for independent algorithm evaluation and scoring. To distribute these data, a website has been created which allows users to download the hyperspectral data and upload their target detection results which are then automatically scored. The Target Detection Blind Test website is located at http://dirs.cis.rit.edu/blindtest/.

For a wide range of application areas, quantifying the ability of a spectral image to satisfy the informational requirements of an application task would be desirable. We propose that this metric may be termed the "image utility" and present a method for assessing the utility of spectral images for the subpixel target detection task. In this paper, we investigate the sensitivity of this utility metric to various image chain parameters. In particular, we examine the effect of small variations of preprocessing and target detection scenario parameters on the assessed utility of a spectral image. We offer a method of quantifying the sensitivity to facilitate a rank ordering of parameter sensitivities. This exploration constitutes an initial step towards gaining a fuller understanding of the key parameters that drive spectral image utility.

Many researchers have presented results showing the empirical performance of target detection algorithms using hyperspectral or synthetic aperture radar imagery. In nearly all cases, these probabilities of detection and false alarm are presented as precise values as opposed to their true nature as estimates of random values. In this letter, we provide analytical tools and examples of computing confidence intervals and regions around these estimates commonly presented as points on receiver operating characteristic (ROC) curves. It is suggested that these tools be adopted by researchers when presenting their results to provide their audience with a quantitative metric for proper interpretation of empirically estimated ROC curves.

The utility of an image is an attribute that describes the ability of that image to satisfy performance requirements for a particular application task. The robust ability to predict the utility of an image for a given application would facilitate sensor design trade studies, provide a basis for tasking image collection activities, and create the foundation for an image archive indexing scheme. In this paper, we examine methods for predicting the utility of spectral images for detecting sub-pixel targets using the constrained energy minimization matched filter detector. The result of our initial work is a prediction of the likelihood of finding a synthetically implanted target in a target-free image in advance of actually applying the detector. We define image utility for the target detection application as the probability of detection at a specified probability of false alarm. We analytically predict this utility for a given image by first estimating statistical parameters directly from the image, then operating on these parameters with the matched filter detector. Three parametric statistical models are used for characterizing the image background: the global Gaussian, the multiple-class Gaussian, and the elliptically contoured t-distributions. The target models come from a library of target materials and are assumed to be multivariate Gaussian with known mean and covariance. We benchmark prediction performance by comparing predicted detection probabilities to empirical results obtained by applying the detector to the data for two HYDICE images. Our longer term objective is to build on this initial result by developing a more general spectral image quality and utility framework, and specific metrics for the prediction of utility across many potential applications.

In many situations it is desirable and advantageous to acquire medical images in more than one modality. For example positron emission tomography can be used to acquire functional data while magnetic resonance imaging can be used to acquire morphological data. In some situations a side by side comparison of the images provides enough information, but in other situations it may be considered a necessity to have the exact spatial relationship between the modalities presented to the observer. In order to accomplish this, the images need to first be registered and then combined (fused) to create a single image. In this paper we discuss the options for performing such fusion in the context of multimodal breast imaging.

This paper highlights the complementary nature of SAR and HSI data in the context of a trafficability assessment. To perform the assessment, different types of classification on the two data sets were performed and fused at the decision level. Results show different strengths for each data set and prove the advantage of using both HSI and SAR data in trafficability assessment.

Multi- and hyperspectral imaging systems have found utility in a variety of Earth remote sensing applications. Much research has focused on developing new ways to process the data and obtain improved results in a given application. However, relatively little research has focused on the open question of what is the optimum performance possible in a given situation. The work discussed in this paper is aimed at exploring that question through analytical modeling and performance trade studies. We show example analysis results that indicate performance floors where no further improvement is possible due to improved spatial resolution or signal-to-noise ratios in given analysis tasks.

As an aid to the requirements analysis of future spaceborne hyperspectral imaging systems, an example study is presented which uses an analytical performance prediction model to study application performance as a function of system parameters. In particular, the forecasting and analysis of spectroradiometric system performance (FASSP) model is used to refine requirements for spatial resolution, spectral resolution, and aperture size in an unresolved road detection application. Results show roads as small as 5 meters could be detected with a system having 5 meter ground resolution, 10 nm spectral resolution, and a 0.25 meter aperture operating from 450 km altitude.

In support of hyperspectral sensor system design and parameter tradeoff investigations, an analytical end-to-end remote sensing system performance forecasting model has been extended to cover the visible through longwave infrared portion of the optical spectrum (0.4-14 /spl mu/m). The model uses statistical descriptions of surface spectral reflectances/emissivities and temperature variations in a scene and propagates them through the effects of the atmosphere, the sensor, and processing transformations. A resultant system performance metric is then calculated based on these propagated statistics. This work presents theory for the analytical transformation of surface statistics to at-sensor spectral radiance statistics for a downward-looking hyperspectral sensor observing both reflected sunlight and thermally emitted radiation. Comparisons of the model predictions with measurements from an airborne hyperspectral sensor are presented. Example parameter trades are included to show the utility of the model for applications in sensor design and operation.

The compensation for atmospheric effects in the VNIR/SWIR has reached a mature stage of development with many algorithms available for application (ATREM, FLAASH, ACORN, etc.). Compensation of LWIR data is the focus of a number of promising algorithms. A gap in development exists in the MWIR where little or no atmospheric compensation work has been done yet an increased interest in MWIR applications is emerging. To obtain atmospheric compensation over the full spectrum (visible through LWIR), a better understanding of the radiative effects in the MWIR is needed. The MWIR is characterized by a unique combination of reduced solar irradiance and low thermal emission (for typical emitting surfaces), both providing relatively equal contributions to the daytime MWIR radiance. In the MWIR and LWIR, the compensation problem can be viewed as two interdependent processes: compensation for the effects of the atmosphere and the uncoupling of the surface temperature and emissivity. The former requires calculations of the atmospheric transmittance due to gases, aerosols, and thin clouds and the path radiance directed towards the sensor (both solar scattered and thermal emissions in the MWIR). A framework for a combined MWIR/LWIR compensation approach is presented where both scattering and absorption by atmospheric particles and gases are considered.

In support of hyperspectral sensor system design and parameter tradeoff investigations, an analytical end-to-end remote sensing system performance forecasting model has been extended to cover the visible and near infrared through longwave infrared portion of the optical spectrum (0.4 to 14 /spl mu/m). The model takes statistical descriptions of surface spectral reflectances and temperature variations in a scene and propagates them through the effects of the atmosphere, the sensor, and processing transformations. A resultant system performance metric is then calculated. This paper presents the theory for analytically transforming surface statistics to at-sensor spectral radiance statistics for a downward-looking hyperspectral sensor observing both reflected sunlight and thermally emitted radiation. Comparisons of the model's predictions with measurements from an airborne hyperspectral sensor are presented. An example is included to show the model's utility in understanding the magnitude of full spectrum radiance components.

Previously, an analytical end-to-end spectral imaging system model has been developed. The model is constructed around the propagation of spectral statistics from the scene, through the sensor, and processing transformations to lead to prediction of a performance metric. In this analytical framework the description of the class statistics has been by their spectral mean vector and spectral covariance matrix (first and second order statistics). This representation is only strictly accurate when the underlying classes are Gaussian in nature. While some background classes fall into this category, many have been observed to be nonGaussian in nature. As a work-around for this limitation, we have often formed subclasses in the background, which when combined form a "composite" background class that can be multimodal. However, we have observed in estimates of empirical data distributions that unimodal backgrounds often have longer tails than those predicted by the Gaussian distribution. Recently, it has been demonstrated that a family of distributions, known as the elliptically contoured multivariate t-distributions, can provide an accurate depiction of empirically observed backgrounds. These distributions are parameterized by their multivariate mean vector and covariance matrix, but also by a degree of freedom parameter, M. By varying M, excellent fits to empirical distributions have been observed. Another key feature of these distributions is that the number of degrees of freedom has been shown to be invariant to linear transformations. Since the analytical model operates by performing a sequence of linear transformations on the statistics, the input value of M is preserved and can be used at any stage of the model to represent the class statistics. This paper describes an implementation of the elliptically contoured t-distributions to represent background classes in the end-to-end system model. The functional form and examples of the t-distributions are shown. Results are presented comparing predictions of target detection performance using backgrounds modeled by multiclass Gaussian distributions with the new elliptical-t distributions.

The quantitative forecasting of spectral imaging system performance is an important capability. The ability to accurately predict the effects on utility of the data due to scene conditions, sensor performance, or even algorithm parameters, can be very important. To this end, an analytical modeling tool has been under development to predict end-to-end spectroradiometric remote sensing system performance, and to understand the relative impact of various system parameters on that performance. Recently, data were collected by NASA's EO-1 Hyperion spaced-based hyperspectral imager over an area in Southern California including spatially resolved buildings of known size. The area of interest was also imaged with previous low-altitude overflights of NASA/JPL's AVIRIS airborne imaging spectrometer. The AVIRIS data provided an opportunity to investigate the accuracy of unmixing analysis applied to the Hyperion image as well as to serve as a source of input data in model forecasts. This paper describes the results of analysis of the remotely sensed data as well as comparisons to predictions made by the analytical performance prediction model. While the empirical analyses provide point results in terms of the abundance of the buildings per pixel, the model predicts the anticipated variation in the abundance estimates given inherent variability of the building roof material and nearby backgrounds. The model is also exercised to show the impact on the abundance estimates from various remote sensing system parameters including sensor noise, radiometric calibration error, and the number of endmembers assumed in the unmixing algorithm. In the example studied, the natural surface variability and the use of endmembers in the unmixing that were not present in the scene were found to have the most impact on the abundance estimates.

The quantitative forecasting of hyperspectral system performance is an important capability at every stage of system development including system requirement definition, system design, and sensor operation. In support of this, Lincoln Laboratory has been developing an analytical modeling tool to predict end-to-end spectroradiometric remote sensing system performance. Recently, the model has been extended to more accurately depict complex natural scenes by including multiple classes in the target pixel through the use of a linear mixing model. Additionally, a linear unmixing algorithm has been implemented to predict retrieved fractional abundances and their associated errors due to both natural variability and corrupting noise sources. This paper describes the details of this multiple target class model enhancement. Comparisons are presented between the model predictions and measured spectral radiances, as well as unmixing results obtained from data collected by NASA's EO-1 Hyperion space-based hyperspectral sensor. Additionally, results of an analysis using the enhanced model are presented to show the sensitivity of end member fractional abundance estimates to system parameters using linear unmixing techniques.

Data from multispectral and hyperspectral imaging systems have been used in many applications including land cover classification, surface characterization, material identification, and spatially unresolved object detection. While these optical spectral imaging systems have provided useful data, their design and utility could be further enhanced by better understanding the sensitivities and relative roles of various system attributes; in particular, when application data product accuracy is used as a metric. To study system parameters in the context of land cover classification, an end-to-end remote sensing system modeling approach was previously developed. In this paper, we extend this model to subpixel object detection applications by including a linear mixing model for an unresolved object in a background and using object detection algorithms and probability of detection (P/sub D/) versus false alarm (P/sub FA/) curves to characterize performance. Validations with results obtained from airborne hyperspectral data show good agreement between model predictions and the measured data. Examples are presented which show the utility of the modeling approach in understanding the relative importance of various system parameters and the sensitivity of P/sub D/ versus P/sub FA/ curves to changes in the system for a subpixel road detection scenario.

To help better understand the problems of specifying data acquisition parameters and extracting desired information from the voluminous data, research has been focused on understanding the remote sensing process as a system and investigating the interrelated effects of various parameters. A system model is used to explore system parameter trade-offs for a model sensor based on the High-Resolution Imaging Spectrometer (HIRIS). Radiometric performance was studied, along with the effect on classification accuracy of several system parameters. The atmosphere and sensor have significant effects on the mean received signal and noise performance. The effect of random uncorrelated errors in the radiometric calibration of the detector array is discussed. Accurate pixel-to-pixel relative radiometric calibration and the use of the image motion compensation (IMC) option are seen to improve classification accuracy. The selection of feature sets based on combining spectral bands was studied under a variety of observational conditions.>

The authors present a system model for the remote sensing process and some results that yield insight into the process. Key results include interrelations between the atmosphere, sensor noise, sensor view angle and scattered path radiance and their influence on classification accuracy of the ground cover type. Also included are results indicating the tradeoffs in ground cell size and surface spatial correlation and their effect on classification accuracy. >