In this paper, we present a scene-based nouniformity correction (NUC) method using a modified adaptive least mean square (LMS) algorithm with a novel gating operation on the updates. The gating is designed to significantly reduce ghosting artifacts produced by many scene-based NUC algorithms by halting updates when temporal variation is lacking. We define the algorithm and present a number of experimental results to demonstrate the efficacy of the proposed method in comparison to several previously published methods including other LMS and constant statistics based methods. The experimental results include simulated imagery and a real infrared image sequence. We show that the proposed method significantly reduces ghosting artifacts, but has a slightly longer convergence time.

The use of hyperspectral imaging (HSI) technology to support a variety of civilian, commercial, and military remote sensing applications, is growing. The rich spectral information present in HSI allows for more accurate ground cover identification and classification than with panchromatic or multispectral imagery. One class of problems where hyperspectral images can be exploited, even when no a priori information about a particular ground cover class is available, is anomaly detection. Here spectral outliers (anomalies) are detected based on how well each hyperpixel (spectral irradiance vector for a given pixel position) fits within some background statistical model. Spectral anomalies may correspond to areas of interest in a given scene. In this work, we compare several anomaly detectors found in the literature in novel experiments. In particular, we study the performance of the anomaly detectors in detecting several man-made painted panels in a natural background using visible/near-infrared hyperspectral imagery. The data have been collected over the course of a nine month period, allowing us to test the robustness of the anomaly detectors with seasonal change. The detectors considered include the simple Gaussian anomaly detector, a Gaussian mixture model (GMM) anomaly detector, and the cluster-based anomaly detector (CBAD). We examine the effect of the number of components for the GMM and the number of clusters for the CBAD. Our preliminary results suggest that the use of a CBAD yields the best results for our data.

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