Here is what we have done in this study:
1). Our previous results of spatio-temporal fusion for target classification have been further developed for target detection. (SPIE AeroSense, Vol. 4731, pp. 204-215, April, 2002)
2). Different temporal integration (fusion) strategies have been developed and compared, including pre-detection integration (such as additive, multiplicative, MAX, and MIN fusions), as well as the traditional post-detection integration (the persistency test).
3). In our 2nd study, The temporal correlation and non-stationary properties of sensor noise have been investigated using sequences of imagery collected by an IR (256x256) sensor looking at different scenes (trees, grass, roads, buildings, etc.).
4). The natural noise extracted from the IR sensor, as well as noise generated by a computer with Gaussian and Rayleigh distributions have been used to test and compare different temporal integration strategies.
Some preliminary results are summarized here:
1). Both the pre- and post-detection temporal integrations can considerably improve target detection by integrating only 3~5 time frames (tested by real sensor noise as well as computer generated noise).
2). The detection results can be further improved by combining both the pre- and post-detection temporal integrations.
3). The sensor noise at most (> 95%) of the sensor pixels are near stationary and un-correlated between pixels as well as (almost) un-correlated across time frames under a good weather condition.
4). The noise at a few pixels near some surface edges has shown non-stationary properties (with increasing or decreasing mean across time).