Search

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]

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]

Infrared search and track (IRST) systems based on 3-D velocity filters are quite sensitive to the random motion that remains after egomotion compensation has occurred (jitter). These systems are designed to detect small targets at long range and they often employ track-before-detect (TBD) strategies to integrate the target signal along consecutive frames. Jitter causes frame misalignment and leads to a reduction of the detection performance. We analyze the effects of jitter on the false alarm and detection probabilities and on the detection range. The analytical expressions here derived are a valuable tool both in the analysis and synthesis of IRST systems based on 3-D velocity filters. [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]