We introduce key concepts and issues including the effects of atmospheric propagation upon the data, spectral variability, mixed pixels, and the distinction between classification and detection algorithms. Detection algorithms for full pixel targets are developed using the likelihood ratio approach. Subpixel target detection, which is more challenging due to background interference, is pursued using both statistical and subspace models for the description of spectral variability. Finally, we provide some results which illustrate the performance of some detection algorithms using real hyperspectral imaging (HSI) data. Furthermore, we illustrate the potential deviation of HSI data from normality and point to some distributions that may serve in the development of algorithms with better or more robust performance. We therefore focus on detection algorithms that assume multivariate normal distribution models for HSI data.

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Detection algorithms for hyperspectral imaging applications

■ This article presents an overview of the theoretical and practical issues associated with the development, analysis, and application of detection algorithms to exploit hyperspectral imaging data. We focus on techniques that exploit spectral information exclusively to make decisions regarding the type of each pixel—target or nontarget—on a pixel-by-pixel basis in an image. First we describe the fundamental structure of the hyperspectral data and explain how these data influence the signal models used for the development and theoretical analysis of detection algorithms. Next we discuss the approach used to derive detection algorithms, the performance metrics necessary for the evaluation of these algorithms, and a taxonomy that presents the various algorithms in a systematic manner. We derive the basic algorithms in each family, explain how they work, and provide results for their theoretical performance. We conclude with empirical results that use hyperspectral imaging data from the HYDICE and Hyperion sensors to illustrate the operation and performance of various detectors.

Hyperspectral Image Processing for Automatic Target Detection Applications

We use the theory of generalized likelihood ratio tests (GLRTs) to adapt the matched subspace detectors (MSDs) of Scharf (1991) and of Scharf and Frielander (1994) to unknown noise covariance matrices. In so doing, we produce adaptive MSDs that may be applied to signal detection for radar, sonar, and data communication. We call the resulting detectors adaptive subspace detectors (ASDs). These include Kelly's (1987) GLRT and the adaptive cosine estimator (ACE) of Kaurt and Scharh (see ibid., vol.47, p.2538-41, 1999) and of Scharf and McWhorter (see Proc. 30th Asilomar Conf. Signals, Syst., Comput., Pacific Grove, CA, 1996) for scenarios in which the scaling of the test data may deviate from that of the training data. We then present a unified analysis of the statistical behavior of the entire class of ASDs, obtaining statistically identical decompositions in which each ASD is simply decomposed into the nonadaptive matched filter, the nonadaptive cosine or t-statistic, and three other statistically independent random variables that account for the performance-degrading effects of limited training data.

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Adaptive subspace detectors

This tutorial provides a brief overview of space-time adaptive processing (STAP) for radar applications We discuss space-time signal diversity and various forms of the adaptive processor, including reduced-dimension and reduced-rank STAP approaches Additionally, we describe the space-time properties of ground clutter and noise-jamming, as well as essential STAP performance metrics We conclude this tutorial with an overview of some current STAP topics: space-based radar, bistatic STAP, knowledge-aided STAP, multi-channel synthetic aperture radar and non-sidelooking array configurations

A STAP overview

The constant false alarm rate (CFAR) matched subspace detector (CFAR MSD) is the uniformly most-powerful-invariant test and the generalized likelihood ratio test (GLRT) for detecting a target signal in noise whose covariance structure is known but whose level is unknown. Previously, the CFAR adaptive subspace detector (CFAR ASD), or adaptive coherence estimator (ACE), was proposed for detecting a target signal in noise whose covariance structure and level are both unknown and whose covariance structure is estimated with a sample covariance matrix based on training data. We show here that the CFAR ASD is GLRT when the test measurement is not constrained to have the same noise level as the training data, As a consequence, this GLRT is invariant to a more general scaling condition on the test and training data than the well-known GLRT of Kelly (1986).

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Wai-Sheou Chen

Papers

A new CFAR detection test for radar