Quantitative and robust metrics are required to objectively compare the performance of algorithms within a general functional class. This is especially true for classification algorithms that cluster and label data using spectral features. This is because spectral algorithms are usually based on a finite set of assumptions about the radiative transfer phenomenology. Thus, a suite of algorithms is needed to achieve a generalized and robust processing chain that performs well under all operational scenarios of interest. An adaptive processing chain that automatically selects the optimal combination of algorithms to generate a product of prescribed quality provides a framework for operational applications. To this end, we have developed Measures of Effectiveness (MOE's) and Figures of Merit (FOM's) that can quantitatively and objectively select the appropriate algorithm automatically. The FOM's are a weighted sum of MOE's, which are performance metrics such as the tightness and dissimilarity of clusters. We have also defined scene and sensor parameters that quantify a subset of factors that affect algorithm performance. Functional relationships between FOM's and MOE's and between the FOM's/MOE's and the scene/sensor parameters were also established. These functional relationships allow users to predict the expected classification product quality given a specific operational scenario based on a performance model that also automates the processing chain. Initial results of an application of this approach to hyperspectral data indicate that FOM's can be predicted with high accuracy with choices made correctly as high as 89% of the time depending on the FOM definitions. The results were obtained over a wide range of operational scenarios.
KEYWORDS: Sensors, Signal to noise ratio, Electrons, Interference (communication), Long wavelength infrared, Imaging systems, Signal processing, Charge-coupled devices, Capacitors, Reflectivity
Panchromatic, multispectral, and hyperspectral image sensors spanning the visible to longwave IR (LWIR) regions of the electromagnetic spectrum are finding increased application in advanced DOD, civil, scientific, and commercial space- based programs. Research and development advancing the state-of-the-art in visible to LWIR focal plane technology requires a careful understanding of system level requirements and a methodology for the translation of these requirements to focal pane specifications. At the focal plane level, signal-to-noise based performance is generally defined in terms of wavelength dependent noise equivalent irradiance and dynamic range specifications under conditions dictated by the system application. In this paper we illustrate a process that starts with system level performance requirements and results in focal plane performance requirements. The input spectral radiances were determined with the MODTRAN atmospheric code coupled with simple sensor and focal pane signal and noise models. The process is illustrated with two different space-based sensor examples, resulting in very different focal plane designs, configurations, and physical operating conditions. Finally, these characteristics were translated to focal pane electro- optical, thermal and electronic design parameters such as: spectral quantum efficiency, integration capacitance values and areas, and likely pixel unit-cell circuit selections.
KEYWORDS: Databases, Sensors, Simulation of CCA and DLA aggregates, Data modeling, Atmospheric modeling, Earth's atmosphere, Black bodies, Atmospheric physics, Atmospheric sensing, Temperature metrology
A new method for atmospheric compensation of longwave infrared (LWIR) hyperspectral images is presented. The technique exploits the large amount of data in hyperspectral images to obtain the most information about atmospheric and surface parameters of interest. This is done with Canonical Correlation Analysis (CCA) by casting the problem onto a multivariate framework. The procedure accounts for the joint effects of surface and atmospheric radiation, thus addressing the complex interaction between the Earth’s surface properties, thermodynamic state, and the atmosphere. After atmospheric compensation, the calculated surface radiance is used to estimate temperature and emissivity. The technique was tested with radiative transfer model simulations and airborne multispectral data. Results obtained from MODTRAN simulations and the MODerate resolution Imaging Spectrometer (MODIS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (MASTER) airborne sensor show that it is feasible to retrieve land surface temperature and emissivity with 1°K and 0.01 accuracies, respectively.
Conference Committee Involvement (3)
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI
28 March 2005 | Orlando, Florida, United States
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X
12 April 2004 | Orlando, Florida, United States
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX
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