Thomas W. Cooley

MODTRAN5: a reformulated atmospheric band model with auxiliary species and practical multiple scattering options

The MODTRAN5 radiation transport (RT) model is a major advancement over earlier versions of the MODTRAN atmospheric transmittance and radiance model. New model features include (1) finer spectral resolution via the Spectrally Enhanced Resolution MODTRAN (SERTRAN) molecular band model, (2) a fully coupled treatment of auxiliary molecular species, and (3) a rapid, high fidelity multiple scattering (MS) option. The finer spectral resolution improves model accuracy especially in the mid- and long-wave infrared atmospheric windows; the auxiliary species option permits the addition of any or all of the suite of HITRAN molecular line species, along with default and user-defined profile specification; and the MS option makes feasible the calculation of Vis-NIR databases that include high-fidelity scattered radiances.

MODTRAN5: 2006 update

The MODTRAN5 radiation transport (RT) model is a major advancement over earlier versions of the MODTRAN atmospheric transmittance and radiance model. New model features include (1) finer spectral resolution via the Spectrally Enhanced Resolution MODTRAN (SERTRAN) molecular band model, (2) a fully coupled treatment of auxiliary molecular species, and (3) a rapid, high fidelity multiple scattering (MS) option. The finer spectral resolution improves model accuracy especially in the mid- and long-wave infrared atmospheric windows; the auxiliary species option permits the addition of any or all of the suite of HITRAN molecular line species, along with default and user-defined profile specification; and the MS option makes feasible the calculation of Vis-NIR databases that include high-fidelity scattered radiances. Validations of the new band model algorithms against line-by-line (LBL) codes have proven successful.

Analysis of Hyperion data with the FLAASH atmospheric correction algorithm

A combination of good spatial and spectral resolution make visible to shortwave infrared spectral imaging from aircraft or spacecraft a highly valuable technology for remote sensing of the Earths surface. Many applications require the elimination of atmospheric effects caused by molecular and particulate scattering; a process known as atmospheric correction, compensation, or removal. The Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) atmospheric correction code derives its physics-based algorithm from the MODTRAN4 radiative transfer code. A new spectra; recalibration algorithm, which has been incorporated into FLAASH, is described. Results from processing Hyperion data with FLAASH are discussed.

Detection Algorithms in Hyperspectral Imaging Systems: An Overview of Practical Algorithms

Hyperspectral imaging applications are many and span civil, environmental, and military needs. Typical examples include the detection of specific terrain features and vegetation, mineral, or soil types for resource management; detecting and characterizing materials, surfaces, or paints; the detection of man-made materials in natural backgrounds for the purpose of search and rescue; the detection of specific plant species for the purposes of counter narcotics; and the detection of military vehicles for the purpose of defense and intelligence. The objective of this article is to provide a tutorial overview of detection algorithms used in current hyperspectral imaging systems that operate in the reflective part of the spectrum (0.4 - 24 μm.) The same algorithms might be used in the long-wave infrared spectrum; however, the phenomenology is quite different. The covered topics and the presentation style have been chosen to illustrate the strong couplings among the underlying phenomenology, the theoretical framework for algorithm development and analysis, and the requirements of practical applications.

MODTRAN4-based atmospheric correction algorithm: FLAASH (fast line-of-sight atmospheric analysis of spectral hypercubes)

Terrain categorization and target detection algorithms applied to Hyperspectral Imagery (HSI) typically operate on the measured reflectance (of sun and sky illumination) by an object or scene. Since the reflectance is a non-dimensional ratio, the reflectance by an object is nominally not affedted by variations in lighting conditions. Atmospheric Correction (also referred to as Atmospheric Compensation, Characterization, etc.) Algorithms (ACAs) are used in application of remotely sensed HSI datat to correct for the effects of atmospheric propagation on measurements acquired by air and space-borne systems. The Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm is an ACA created for HSI applications in the visible through shortwave infrared (Vis-SWIR) spectral regime. FLAASH derives its physics-based mathematics from MODTRAN4.

Hyperspectral Detection Algorithms: Use Covariances or Subspaces?

There are two broad classes of hyperspectral detection algorithms.1, 2 Algorithms in the first class use the spectral covariance matrix of the background clutter; in contrast, algorithms in the second class characterize the background using a subspace model. In this paper we show that, due to the nature of hyperspectral imaging data, the two families of algorithms are intimately related. The link between the two representations of the background clutter is the low-rank of the covariance matrix of natural hyperspectral backgrounds and its relation to the spectral linear mixture model. This link is developed using the method of dominant mode rejection. Finally, the effects of regularization

Hyperspectral matched filter with false-alarm mitigation

One of the fundamental challenges for a hyperspectral imaging surveillance system is the detection of sub-pixel objects in background clutter. The background surrounding the object, which acts as interference, provides the major obstacle to successful detection. One algorithm that is widely used in hyperspectral detection and successfully suppresses the background in many situations is the matched filter detector. However, the matched filter also produces false alarms in many situations. We use three simple and well-established concepts-the target-background replacement model, the matched filter, and Mahalanobis distance-to develop the matched filter with false alarm mitigation (MF-FAM), a dual-threshold detector capable of eliminating many matched filter false alarms. We compare this algorithm to the mixture tuned matched filter (MTMF), a popular approach to matched filter false alarm mitigation found in the ENVI® software environment. The two algorithms are shown to produce nearly identical results using real hyperspectral data, but the MF-FAM is shown to be operationally, computationally, and theoretically simpler than the MTMF.

The remarkable success of adaptive cosine estimator in hyperspectral target detection

A challenging problem of major importance in hyperspectral imaging applications is the detection of subpixel targets of military and civilian interest. The background clutter surrounding the target, acts as an interference source that simultaneously distorts the target spectrum and reduces its strength. Two additional limiting factors are the spectral variability of the background clutter and the spectral variability of the target. Since a result in applied statistics is only as reliable as the assumptions from which it is derived, it is important to investigate whether the basic assumptions used for the derivation of matched filter and adaptive cosine estimator algorithms are a reasonable description of the physical situation. Careful examination of the linear signal model used to derive these algorithms and the replacement signal model, which is a more realistic model for subpixel targets, reveals a serious discrepancy between modeling assumptions and the physical world. Despite this discrepancy and additional mismatches between assumed and actual signal and clutter models, the adaptive cosine estimator shows an amazing effectiveness in practical target detection applications. The objective of this paper is an attempt to explain this unbelievable effectiveness using a combination of classical statistical detection theory, geometrical interpretations, and a novel realistic performance prediction model for the adaptive cosine estimator.

On The Spectral Correlation Structure of Hyperspectral Imaging Data

Spectral correlation, as quantified by the elements of the covariance matrix, plays a prominent role in the development of optimum statistical algorithms for hyperspectral data exploitation. Indeed, the most useful statistical models for hyperspectral image modeling, namely the multivariate normal distribution and the multivariate t-distribution, are parameterized by the spectral covariance matrix. The inverse of the covariance matrix, however, also has important interpretations. In this paper, we discuss the properties connected with the inverse covariance matrix and we describe their use in hyperspectral data analysis.

Testing an automated unsupervised classification algorithm with diverse land covers

We test a new automatic unsupervised classification algorithm designed for hyperspectral images. The algorithm automatically determines the number of clusters in the image by finding dense regions of the pixel cloud. A variation on migrating means clustering is used to find the dense regions. Five scenes from an airborne AVIRIS data set are used to test the algorithm. The algorithm successfully finds the dominant land covers and many areally small land covers, such as roads and other man-made structures.