Spectral imaging system analytical model for subpixel object detection

Hsu

2004

SPIE Proceedings

Current image quality approaches are designed to assess the utility of single band images by trained image analysts. While analysts today are certainly involved in the exploitation of spectral imagery, automated tools are generally used as aids in the analysis and offer hope in the future of significantly reducing the timeline and analysis load. Thus, there is a recognized need for spectral image quality metrics that include the effects of automated algorithms.We have begun initial efforts in this area through the use of a parametric modeling tool to gain insight into parameter dependence on system performance in unresolved object detection applications. An initial Spectral Quality Equation (SQE) has been modeled after the National Imagery Interpretation Rating Scale General Image Quality Equation (NIIRS GIQE). The parameter sensitivities revealed through the model-based trade studies were assessed through comparison to analogous studies conducted with available data. This current comparison has focused on detection applications using sensors operating in the VNIR and SWIR spectral regions.The SQE is shown with key image parameters and sample coefficients. Results derived from both model-based trade studies and empirical data analyses are compared. Extensions of the SQE approach to additional application areas such as material identification and terrain classification are also discussed.

Section: Model-based Trade Study and Spectral Quality Equationmentioning

confidence: 99%

Spectral quality metrics for VNIR and SWIR hyperspectral imagery

Hsu

2004

SPIE Proceedings

“…This previous work investigated the tradeoffs between the parameters identified in Section 2 (GRD, spectral resolution, and SNR), using an analytical spectral performance prediction model [10] that ran quickly and allowed a large number of parameter combinations to be studied efficiently. A set of observation scenarios with three targets and three backgrounds were defined, and then a large number of trade studies conducted.…”

Section: Review Of Detection Spectral Quality Equationmentioning

confidence: 99%

Spectral quality metrics for terrain classification

Hsu

2004

SPIE Proceedings

Current image quality approaches are designed to assess the utility of single band images by trained image analysts. While analysts today are certainly involved in the exploitation of spectral imagery, automated tools are generally used as aids in the analysis and offer hope in the future of significantly reducing the analysis timeline and analyst work load. Thus, there is a recognized need for spectral image quality metrics that include the effects of automated algorithms.Previously, we have reported on candidate approaches for spectral quality metrics in the context of unresolved object detection. We have continued these efforts through the use of empirical trade studies in the context of ground cover terrain classification. HYDICE airborne hyperspectral imagery have been analyzed for the effects on scene classification accuracy of spatial resolution, signal-to-noise ratio, and number of spectral channels. Various classification algorithms including Gaussian maximum likelihood, spectral angle mapper, and Euclidean minimum distance have been considered. Performance metrics included classification accuracy, confusion matrices, and the Kappa coefficient. An extension of the previously developed Spectral Quality Equation (SQE) has been developed for the terrain classification application.As expected, the accuracy of terrain classification shows only modest sensitivity to the parameters considered, except at the extreme cases of high noise, few bands, and small ground resolution. However, these results are useful in continuing to develop the quantitative relationships necessary for characterizing the quality of spectral imagery in various applications.

“…Our methodology predicts the likelihood of finding a synthetically implanted target in a target-free image in advance of applying the detector. It differs from the Forecasting and Analysis of Spectroradiometric System Performance (FASSP) analytical model-based approach to prediction described in [1][2][3] in that it begins with a real image rather than a statistical description of a notional imaging scenario, thereby capturing the effects of the scene, atmosphere, and sensor in estimated statistical parameters. These multivariate statistical parameters are then transformed, rather than the data itself, by the CEM detector into parameters that describe the scalar output test statistic.…”

Section: Introductionmentioning

confidence: 99%

Spectral image utility prediction

Stefanou

2007 IEEE International Geoscience and Remote Sensing Symposium

2007

Abstract-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.