Robert Sundberg

Validation of the QUick atmospheric correction (QUAC) algorithm for VNIR-SWIR multi- and hyperspectral imagery

We describe a new visible-near infrared short-wavelength infrared (VNIR-SWIR) atmospheric correction method for multi- and hyperspectral imagery, dubbed QUAC (QUick Atmospheric Correction) that also enables retrieval of the wavelength-dependent optical depth of the aerosol or haze and molecular absorbers. It determines the atmospheric compensation parameters directly from the information contained within the scene using the observed pixel spectra. The approach is based on the empirical finding that the spectral standard deviation of a collection of diverse material spectra, such as the endmember spectra in a scene, is essentially spectrally flat. It allows the retrieval of reasonably accurate reflectance spectra even when the sensor does not have a proper radiometric or wavelength calibration, or when the solar illumination intensity is unknown. The computational speed of the atmospheric correction method is significantly faster than for the first-principles methods, making it potentially suitable for real-time applications. The aerosol optical depth retrieval method, unlike most prior methods, does not require the presence of dark pixels. QUAC is applied to atmospherically correction several AVIRIS data sets and a Landsat-7 data set, as well as to simulated HyMap data for a wide variety of atmospheric conditions. Comparisons to the physics-based Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) code are also presented.

A new method for atmospheric correction and aerosol optical property retrieval for VIS-SWIR multi- and hyperspectral imaging sensors: QUAC (QUick atmospheric correction)

Abstract : We describe a new VNIR-SWIR atmospheric correction method for multi- and hyperspectral imagery, dubbed QUAC (QUick Atmospheric Correction) that also enables retrieval of the wavelength-dependent optical depth of the aerosol or haze and molecular absorbers. It determines the atmospheric compensation parameters directly from the information contained within the scene using the observed pixel spectra. The approach is based on the empirical finding that the spectral standard deviation of a collection of diverse material spectra, such as the endmember spectra in a scene, is essentially spectrally flat. It allows the retrieval of reasonably accurate reflectance spectra even when the sensor does not have a proper radiometric or wavelength calibration, or when the solar illumination intensity is unknown. The computational speed of the atmospheric correction method is significantly faster than for the first-principles methods, making it potentially suitable for realtime applications. The aerosol optical depth retrieval method, unlike most prior methods, does not require the presence of dark pixels. In this paper, QUAC is applied to atmospherically correction several AVIRIS data sets. Comparisons to the physics-based FLAASH code are also presented.

Improved reflectance retrieval from hyper-and multispectral imagery without prior scene or sensor information

We describe improvements to a recently developed VNIR-SWIR atmospheric correction method for hyper- and multispectral imagery, dubbed QUAC (QUick Atmospheric Correction). It determines the atmospheric compensation parameters directly from the information contained within the scene using the observed pixel spectra. The newest implementation of QUAC is based on the assumption that the average reflectance of a collection of diverse material spectra, such as the endmember spectra in a scene, is effectively scene independent. This enables the retrieval of reasonably accurate reflectance spectra even when the sensor does not have a proper radiometric or wavelength calibration, or when the solar illumination intensity is unknown. The computational speed of the atmospheric correction method is significantly faster than for the first-principles methods, making it potentially suitable for real-time applications on aircraft and spacecraft. QUAC is applied to a diverse collection of hyper- and multispectral data sets and the results are compared to those obtained with the physics-based atmospheric correction code FLAASH (Fast Line of sight Atmospheric Analysis of Spectral Hypercubes).

Automated optimal channel selection for spectral imaging sensors

A method of optimizing the selection of spectral channels in a spectral-spatial remote sensor has been developed that is applicable to the design of multispectral, hyperspectral and ultra spectral resolution sensors. The approach is based on an end member analysis technique that has been refined to select the most information dense channels. The algorithm operates sequentially and at any step in the sequence, the channel selected is the most independent form all previously selected channels. After the channel selection process, highly correlated channels, which are contiguous to those selected, can be merged to form bands. This process increases the signal to noise for the new broader spectral bands. The resulting bands, potentially of unequal width and spacing, collect the most uncorrelated spectral information present in the data. The band selection provides a physical interpretation of the data and has applications in spectral feature selection and data compression.

In-scene-based atmospheric correction of uncalibrated VISible-SWIR (VIS-SWIR) hyper- and multispectral imagery

The QUAC (Quick Atmospheric Correction) algorithm for in-scene-based atmospheric correction of VIS-SWIR (VISible-Short Wave InfraRed) Multi- and Hyperspectral Imagery (MSI and HSI) is reviewed and applied to radiometrically uncalibrated data. Quite good agreement was previously demonstrated for the retrieved pixel spectral reflectances between QUAC and the physics-based atmospheric correction code FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) for a variety of HSI and MSI data cubes. In these code-to-code comparisons, all the data cubes were obtained with well-calibrated sensors. However, many sensors operate in an uncalibrated manner, precluding the use of physics-based codes to retrieve surface reflectance. The ability to retrieve absolute spectral reflectances from such sensors would significantly increase the utility of their data. We apply QUAC to calibrated and uncalibrated versions of the same Landsat MSI data cube, and demonstrate nearly identical retrieved spectral reflectances for the two data sets.

Fast Monte Carlo Full Spectrum Scene Simulation

This paper discusses the formulation and implementation of an acceleration approach for the MCScene code, a high fidelity model for full optical spectrum (UV to LWIR) hyperspectral image (HSI) simulation. The MCScene simulation is based on a Direct Simulation Monte Carlo approach for modeling 3D atmospheric radiative transport, as well as spatially inhomogeneous surfaces including surface BRDF effects. The model includes treatment of land and ocean surfaces, 3D terrain, 3D surface objects, and effects of finite clouds with surface shadowing. This paper will review an acceleration algorithm that exploits spectral redundancies in hyperspectral images. In this algorithm, the full scene is determined for a subset of spectral channels, and then this multispectral scene is unmixed into spectral end members and end member abundance maps. Next, pure end member pixels are determined at their full hyperspectral resolution, and the full hyperspectral scene is reconstructed from the hyperspectral end member spectra and the multispectral abundance maps. This algorithm effectively performs a hyperspectral simulation while requiring only the computational time of a multispectral simulation. The acceleration algorithm will be demonstrated, and errors associated with the algorithm will be analyzed.

Quick atmospheric correction (QUAC) code for VNIR-SWIR spectral imagery: Algorithm details

We describe an in-scene method for VNIR-SWIR atmospheric correction for multi- and hyperspectral imagery, dubbed QUAC (QUick Atmospheric Correction). It determines the atmospheric compensation parameters directly from the information contained within the scene using the observed pixel spectra. The approach is based on the empirical finding that the mean spectrum of a collection of diverse material spectra, such as the endmember spectra in a scene, is essentially invariant from scene to scene. It allows the retrieval of reasonably accurate reflectance spectra even when the sensor does not have a proper radiometric or wavelength calibration, or when the solar illumination intensity is unknown. The computational speed of the atmospheric correction method is significantly faster than for the first-principles methods, making it potentially suitable for real-time applications. QUAC is applied to atmospherically correction of example AVIRIS and HyMap data sets. Comparisons to the physics-based FLAASH code are shown.

Atmospheric Compensation of Extreme Off-Nadir Hyperspectral Imagery from Hyperion

Compared to nadir viewing, off-nadir viewing of the ground from a high-altitude platform provides opportunities to increase area coverage and to reduce revisit times, although at the expense of spatial resolution. In this study, the ability to atmospherically compensate off-nadir hyperspectral imagery taken from a space platform was evaluated for a worst-case viewing geometry, using EO-1 Hyperion data collected with an off-nadir angle of 63° at the sensor, corresponding to six air masses along the line of sight. Reasonable reflectance spectra were obtained using both first-principles (FLAASH) and empirical (QUAC) atmospheric-compensation methods. Some refinements to FLAASH that enable visibility retrievals with highly off-nadir imagery, and also improve accuracy in nadir viewing, were developed and are described.

Extraction of spatial and spectral scene statistics for hyperspectral scene simulation

A method for extracting statistics from hyperspectral data and generating synthetic scenes suitable for scene generation models is presented. Regions composed of a general surface type with a small intrinsic variation, such as a forest or crop field, are selected. The spectra are decomposed using a basis set derived from spectra present in the scene and the abundances of the basis members in each pixel spectrum found. Statistics such as the abundance means, covariances and channel variances are extracted. The scenes are synthesized using a coloring transform with the abundance covariance matrix. The pixel-to-pixel spatial correlations are modeled by an autoregressive moving average texture generation technique. Synthetic reflectance cubes are constructed using the generated abundance maps, the basis set and the channel variances. Enhancements include removing any pattern from the scene and reducing the skewness. This technique is designed to work on atmospherically-compensated data in any spectral region, including the visible-shortwave infrared HYDICE and AVIRIS data presented here. Methods to evaluate the performance of this approach for generating scene textures include comparing the statistics of the synthetic surfaces and the original data, using a signal-to-clutter ratio metric, and inserting sub-pixel spectral signatures into scenes for detection using spectral matched filters.

Hyperspectral Detection and Identification with Constrained Target Subspaces

Subspace methods for hyperspectral imagery enable detection and identification of targets under unknown environmental conditions by specifying a subspace of possible target spectral signatures (and, optionally, a background subspace) and identifying closely fitting spectra in the image. In this study, detection performance in the thermal infrared (IR) was compared using various constrained and unconstrained basis set expansions of low-dimensional target subspaces. An initial investigation of detection using retrieved atmospheric parameters to reduce subspace size and/or dimensionality has also been performed.