A Comparative Evaluation of Spectral Quality Metrics for Hyperspectral Imagery

Quantitative methods to assess or predict the quality of a spectral image are the subject of a number of current research activities. An accepted methodology would be highly desirable to use for data collection tasking or data archive searches in way analogous to the current uses of the National Imagery Interpretation Rating Scale (NIIRS) General Image Quality Equation (GIQE). A number of approaches to the estimation of quality of a spectral image have been published. An issue with many of these approaches is that they tend to be constructed around specific tasks (target detection, background classification, etc.) While this has often been necessary to make the quality assessment tractable, it is desirable to have a method that is more general. One such general approach is presented in a companion paper (Simmons, et al1). This new approach seeks to get at the heart of the general spectral imagery quality analysis problem – assessing the confidence of an image analyst in performing a specified task with a specific spectral image. In this approach the quality from spatial and spectral aspects of the imagery are treated separately and then a fusion concept known as “semantic transformation” is used to combine the utility, or confidence, from these two aspects into an overall quality metric. This paper compares and contrasts the various methods published in the literature with this new General Spectral Utility Metric (GSUM). In particular, the methods are applied to a target detection problem using data from the airborne HYDICE instrument collected at Forest Radiance I. While the GSUM approach is seen to lead to intuitively pleasing results, its sensitivity to image parameters was not seen to be consistent with previously published approaches. However, this likely resulted more from limitations of the previous approaches than with problems with GSUM. Further studies with additional spectral imaging applications are recommended along with efforts to integrate a performance predication capability into the GSUM framework

Comparisons Between Spectral Quality Metrics and Analyst Performance in Hyperspectral Target Detection

Quantitative methods to assess or predict the quality of a spectral image continue to be the subject of a number of current research activities. An accepted methodology would be highly desirable for use in data collection tasking or data archive searching in ways analogous to the current prediction of panchromatic image quality through the National Imagery Interpretation Rating Scale (NIIRS) using the General Image Quality Equation (GIQE). A number of approaches to the estimation of quality of a spectral image have been published, but most capture only the performance of automated algorithms applied to the spectral data. One recently introduced metric, however, the General Spectral Utility Metric (GSUM), provides for a framework to combine the performance from the spectral aspects together with the spatial aspects. In particular, this framework allows the metric to capture the utility of a spectral image resulting when the human analyst is included in the process. This is important since nearly all hyperspectral imagery analysis procedures include an analyst. To investigate the relationships between candidate spectral metrics and task performance from volunteer human analysts in conjunction with the automated results, simulated images are generated and processed in a blind test. The performance achieved by the analysts is then compared to predictions made from various spectral quality metrics to determine how well the metrics function. The task selected is one of finding a specific vehicle in a cluttered environment using a detection map produced from the hyperspectral image along with a panchromatic rendition of the image. Various combinations of spatial resolution, number of spectral bands, and signal-to-noise ratios are investigated as part of the effort

A Bayesian assessment of an approximate model for unconfined water flow in sloping layered porous media

The prediction of water table height in unconfined layered porous media is a difficult modelling problem that typically requires numerical simulation. This paper proposes an analytical model to approximate the exact solution based on a steady-state Dupuit–Forchheimer analysis. The key contribution in relation to a similar model in the literature relies in the ability of the proposed model to consider more than two layers with different thicknesses and slopes, so that the existing model becomes a special case of the proposed model herein. In addition, a model assessment methodology based on the Bayesian inverse problem is proposed to efficiently identify the values of the physical parameters for which the proposed model is accurate when compared against a reference model given by MODFLOW-NWT, the open-source finite-difference code by the U.S. Geological Survey. Based on numerical results for a representative case study, the ratio of vertical recharge rate to hydraulic conductivity emerges as a key parameter in terms of model accuracy so that, when appropriately bounded, both the proposed model and MODFLOW-NWT provide almost identical results

The ontogeny of antipredator behavior: age differences in California ground squirrels (Otospermophilus beecheyi) at multiple stages of rattlesnake encounters

Newborn offspring of animals often exhibit fully functional innate antipredator behaviors, but they may also require learning or further development to acquire appropriate responses. Experience allows offspring to modify responses to specific threats and also leaves them vulnerable during the learning period. However, antipredator behaviors used at one stage of a predator encounter may compensate for deficiencies at another stage, a phenomenon that may reduce the overall risk of young that are vulnerable at one or more stages. Few studies have examined age differences in the effectiveness of antipredator behaviors across multiple stages of a predator encounter. In this study, we examined age differences in the antipredator behaviors of California ground squirrels (Otospermophilus beecheyi) during the detection, interaction, and attack stages of Pacific rattlesnake (Crotalus oreganus) encounters. Using free-ranging squirrels, we examined the ability to detect free-ranging rattlesnakes, snake-directed behaviors after discovery of a snake, and responses to simulated rattlesnake strikes. We found that age was the most important factor in snake detection, with adults being more likely to detect snakes than pups. We also found that adults performed more tail flagging (a predator-deterrent signal) toward snakes and were more likely to investigate a snake’s refuge when interacting with a hidden snake. In field experiments simulating snake strikes, adults exhibited faster reaction times than pups. Our results show that snake detection improves with age and that pups probably avoid rattlesnakes and minimize time spent in close proximity to them to compensate for their reduced reaction times to strikes