Spatial-spectral filtering for the detection of point targets in multi- and hyperspectral data

Y. Cohen; S. R. Rotman

doi:10.1117/12.598850

1 June 2005 Spatial-spectral filtering for the detection of point targets in multi- and hyperspectral data

Y. Cohen, S. R. Rotman

Proceedings Volume 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI; (2005) https://doi.org/10.1117/12.598850
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

By using multispectral and hyperspectral data for detecting subpixel targets, we are able to exploit for point target detection both the spectral signature and the overall brightness of the target pixel. The standard methods for such detection (for example, the RX algorithm) assume that an accurate measure of the mean and the covariance matrix of the area is available; the deviation of the suspect pixel from these estimates is a measure of the degree that this pixel is a target. These algorithms are particularly difficult to implement in images which contain multiple areas with different underlying statistical distributions. Such images need local estimates at each pixel to calculate the correct mean and covariance matrix. Even so, edge points between areas will still be incorrectly evaluated both because the pixels themselves are mixtures of different backgrounds and because the local estimate of the mean and covariance matrix will be faulty due to the presence of pixels from both distributions in the surrounding areas. We have tried several approaches to lower the false alarms rates in such images. In particular, we have tried raising the threshold for detection in transition areas; in addition, we have used segmentation to better estimate the covariance matrixes of areas of similar pixels. In this work, we propose that the use of a spatial filter on the results of the spectral filter will greatly improve our results. Our experience in many of these images is that the locations of the false alarms tend to be grouped spatially. By raising the threshold in this way, we can eliminate the false alarms; although there will be some areas in which the targets will be noticeably harder to detect, the overall improvement due to the lowering of the false alarm rate and the consequent lowering of the threshold for target detection is notable.

Citation Download Citation

Y. Cohen and S. R. Rotman "Spatial-spectral filtering for the detection of point targets in multi- and hyperspectral data", Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); https://doi.org/10.1117/12.598850

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