<title>Temporal filtering for point target detection in staring IR imagery: II. Recursive variance filter</title>

Spatial and temporal point tracking in real hyperspectral images

“…Type 1 signals are the most problematic, since the DC of such signals does not have an overall fit with a linear model, but depends on piecewise matching of the DC to windows sizes, as explained below. Figures 11,12,13,14,15,16,17,18,19, and 20 illustrate the algorithm's operation on the various signal types, with and without an implemented target. In each case, the DC signal and the estimated variance values (calculated after subtracting the estimated DC from the signal) are also plotted.…”

“…A target signal was implanted into these background signals to simulate a target traversing both clutter and noise-dominated scenes. On the basis of the study of Silverman et al [12] showing that the temporal noise is closely matched to white Gaussian noise, we used white Gaussian noise at various SNRs to test the temporal algorithm. Figure 8 shows the different types of signal used to test the algorithm.…”

“…Our system uses algorithms from two different areas, target detection in hyperspectral imagery [1][2][3][4][5][6][7][8][9] and target tracking in IR sequences [10][11][12][13][14][15][16][17][18][19]. Numerous works have addressed each of these problems separately, but to the best of our knowledge, to date no attempts have been made to combine the two fields.…”

“…Thus, we first transform each hyperspectral cube into a two-dimensional (2D) image using a hyperspectral target detection method. The next step involves a temporal separation of the movie (sequence of images) into sub-movies and the usage of a variance filter (VF) [10][11][12][13] algorithm. The filter detects the presence of targets from the temporal profile of each pixel, while suppressing clutter-specific influences.…”

Analgorithmic Framework for Automatic Detection and Tracking Moving Point Targets in IR Image Sequences

“…There exists various other techniques, viz., hypothesis testing 10,11 , track before detect [12][13][14] , techniques that use both the spatial and temporal information 15,16 surrounded by PuT for accurately estimating the background information. hypothesis testing methods are probabilistic techniques that use predefined models for the background to identify target pixels.…”