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In this paper, we investigate the temperature and emissivity separation (TES) problem from hyperspectral data acquired in the long-wave infrared region (LWIR) of the electromagnetic spectrum. We derive a general class of TES algorithms [subspace-based TES (SBTES)] relying on the assumption that the emissivity spectra of natural and man-made materials can be well represented in a given subspace of the original data space. Specifically, by exploiting the subspace representation and the Gaussian model for the noise affecting LWIR hyperspectral data, we approach TES under a statistical perspective by obtaining the maximum likelihood estimates of both the temperature and the spectral emissivity. The proposed approach originates several algorithms whose specific form depends on the particular basis matrix adopted to address the emissivity subspace. We study the performance of the presented class of algorithms and derive theoretical bounds on the accuracy of the temperature and emissivity estimators. Furthermore, by specifying two basis matrices for the emissivity subspace, we propose two different algorithms within the SBTES class. Finally, we present the results of an extensive experimental analysis carried out over simulated data to assess and compare the performance of the two presented algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a methodology that aims at evaluating the effectiveness of different TES strategies is presented. The methodology takes into account the specific material of interest in the monitored scenario, sensor characteristics, and errors in the atmospheric compensation step. The methodology is proposed in order to predict and analyse algorithms performances during the planning of a remote sensing mission, aimed to discover specific materials of interest in the monitored scenario. As case study, the proposed methodology is applied to a real airborne data set of a suburban scenario. In order to perform the TES problem, three state-of-the-art algorithms, and a recently proposed one, are investigated: Temperature-Emissivity Separation '98 (TES-98) algorithm, Stepwise Refining TES (SRTES) algorithm, Linear piecewise TES (LTES) algorithm, and Optimized Smoothing TES (OSTES) algorithm. At the end, the accuracy obtained with real data, and the ones predicted by means of the proposed methodology are compared and discussed. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the constant false-alarm rate (CFAR) property of the RX anomaly detector which is widely used for the analysis of hyperspectral data. The RX detector relies on an adaptive scheme where the mean vector and the covariance matrix of the background are locally estimated from the image pixels themselves. First, demeaning is accomplished by removing the estimated local background mean value, and then, the covariance matrix is estimated in a homogeneous neighborhood of each pixel. In principle, if the local mean is perfectly removed and the covariance matrix is estimated from background pixels sharing the same covariance matrix, the RX algorithm has the CFAR property, which is highly desirable in practical applications. The CFAR behavior of the algorithm also requires the spatial stationarity of the random noise affecting the hyperspectral image. In data collected by new-generation sensors, such an assumption is not valid because photon noise contribution, which depends on the spatially varying signal level, is not negligible. This has motivated us to analyze the behavior of the RX algorithm with respect to the CFAR property in data affected by signal-dependent (SD) noise. In this paper, we show both theoretically and experimentally that the SD noise is one of the causes of the non-CFAR behavior of the RX detector that we have experienced in many practical situations. We propose a strategy to enhance the robustness of the anomaly detection scheme with respect to the CFAR property based on an adaptive nonlinear transform aimed at reducing the dependence of the noise on the signal level. Experiments on simulated data and real data collected by a new hyperspectral camera are also presented and discussed. [ABSTRACT FROM AUTHOR]

Orthogonal subspace projection (OSP) is a powerful tool for dimensionality reduction (DR) in hyperspectral images (HSIs). In the OSP approach, the basis of the signal subspace must be estimated from the data themselves. Such estimation task is referred to as signal subspace identification (SSI). Most of the SSI methods in the literature are based on the analysis of the data second-order statistics (SOS) and have two main drawbacks: 1) They do not take into account the rare signal components (or rare vectors), 2) they assume that noise is spatially stationary. Rare vectors are those signal components that are present in pixels scarcely represented in the image and linearly independent on the signal components characterizing the rest of the image pixels. SOS-based SSI algorithms estimate the signal subspace addressing mostly the background and ignoring the presence of the rare pixels. This may be detrimental for the performance of detection algorithms when DR is adopted as a pre-processing step in small target detection applications. In this paper, a new technique for SSI in HSIs is presented. The algorithm is developed to account for both the abundant and the rare signal components. The method is derived by assuming a signal-dependent model for the noise affecting the data. This makes the SSI algorithm particularly suitable for the processing of images acquired by new generation sensors where, due to the improved sensitivity of the electronic components, noise includes a signal-dependent term. Results on simulated data are discussed, and the comparison with a recently proposed technique based on the analysis of SOS is performed. Furthermore, the results obtained by applying the SSI algorithm to a real HSI affected by signal-dependent noise are presented and discussed. [ABSTRACT FROM AUTHOR]

In this paper, a new algorithm for striping noise reduction in hyperspectral images is proposed. The new algorithm exploits the orthogonal subspace approach to estimate the striping component and to remove it from the image, preserving the useful signal. The algorithm does not introduce artifacts in the data and also takes into account the dependence on the signal intensity of the striping component. The effectiveness of the algorithm in reducing striping noise is experimentally demonstrated on real data acquired both by airborne and satellite hyperspectral sensors. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of signal subspace estimation for dimensionality reduction (DR) in hyperspectral images in the presence of rare pixels, i.e., pixels that are scarcely represented in the image and containing spectral components that are linearly independent of the background. Most of the classical methods proposed in the literature are based on the analysis second-order statistics (SOS), which are weakly influenced by the rare signals. Therefore, such techniques estimate the signal subspace addressing mostly the background and ignoring the presence of rare pixels. This may reduce the target/background spectral contrast, thus decreasing the detection performance when DR is adopted as preprocessing task in small-target detection applications. In this paper, a new robust algorithm, namely, robust signal subspace estimation (RSSE), is developed, which preserves both abundant and rare signal components. It combines the analysis of SOS and a recent approach based on the analysis of the l2∞ norm. The novel contribution of this paper is twofold. First, the RSSE algorithm is presented, which includes a new iterative procedure to derive the signal subspace and an original statistical method to estimate the data dimensionality. Second, an ad simulation strategy is proposed to assess the performance of signal subspace estimation methods in the presence of rare signal components. The procedure is adopted to compare the RSSE algorithm with a classical technique based on the analysis of SOS. The results obtained by applying the two methods on a real Airborne Visible Infrared Imaging Spectrometer hyperspectral image also presented and discussed. [ABSTRACT FROM AUTHOR]

The problem of full pixel target detection (FPTD) in hyperspectral images assuming that the target spectral signature is known is considered. Most of the detection algorithms proposed in the literature for FPTD have been derived from the additive model (AM) assumption. Since the AM is not appropriate to describe the FPTD problem, we resort to the more realistic replacement target model (RTM). It exploits the fact that in FPTD problems the target completely fills the image pixel obscuring or replacing the background. The derivation of an RTM-based detection strategy is one of the original contributions of this work. It is assumed that both the background and the target classes are characterised by the Gaussian model and that the two classes share the same covariance matrix. Assuming that both the background mean vector and the covariance matrix are unknown, the fully adaptive detector (FAD) is derived. It is shown that the performances of the FAD in typical operating conditions can be approximated by those of the adaptive detector (AD) derived by assuming that the background mean vector is known. The AD is theoretically analysed, its performances are derived and its CFAR behaviour is demonstrated. It is also stated that, in practice, the AD design methodology can be adopted to design FAD automatic target detectors. This issue is proved by simulation in a case study in which the model parameters are estimated from a hyperspectral dataset acquired by the airborne visible/infrared imaging spectrometer (AVIRIS). [ABSTRACT FROM AUTHOR]

A new algorithm is presented for the detection of airborne targets by means of infrared sensors operating in naval surveillance scenarios. The proposed algorithm consists of two steps: background clutter removal and detection over the residual clutter. The algorithm is fully automatic and its implementation does not require the tuning of any parameter other than the threshold for setting the probability of false alarm. The algorithm performance is investigated by means of a sequence of experimental IR images taken in a typical maritime environment. The results show that the proposed algorithm outperforms a standard detection algorithm specifically tailored to the analysed scenario. A statistical analysis of the experimental data is also performed to validate the hypotheses used to derive the detection algorithm. [ABSTRACT FROM AUTHOR]

The LMS-based adaptive non-uniformity correction (NUC) technique, known in the literature as Scribner's algorithm, is an efficient method to mitigate the presence of fixed pattern noise in video sequences acquired by thermal cameras. Unfortunately, ghosting artefacts can be generated from the process of NUC depending on the edges that characterise the scene inside the sensor's field of view. Introduced is a de-ghosting technique based on the computation of the statistics of the error signal employed in the Scribner's algorithm. Such a technique is characterised by a small computational load which is the most important requirement for real-time applications. Experimental results demonstrate its good performance and de-ghosting capabilities. [ABSTRACT FROM AUTHOR]